c ○ World Scientific Publishing Company A SURVEY ON PATTERN RECOGNITION APPLICATIONS OF SUPPORT VECTOR MACHINES
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paper_title: Typhoon Analysis and Data Mining with Kernel Methods

paper_content:

The analysis of the typhoon is based on the manual pattern recognition of cloud patterns on meteorological satellite images by human experts, but this process may be unstable and unreliable, and we think could be improved by taking advantage of both the large collection of past observations and the state-of-the-art machine learning methods, among which kernel methods, such as support vector machines (SVM) and kernel PCA, are the focus of the paper. To apply the "learning-from-data" paradigm to typhoon analysis, we built the collection of more than 34,000 well-framed typhoon images to be used for spatio-temporal data mining of typhoon cloud patterns with the aim of discovering hidden and unknown regularities contained in large image databases. In this paper, we deal with the problem of visualizing and classifying typhoon cloud patterns using kernel methods. We compare preliminary results with baseline algorithms, such as principal component analysis and a k-NN classifier, and discuss experimental results with the future direction of research.
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paper_title: Support vector machines for spam categorization

paper_content:

We study the use of support vector machines (SVM) in classifying e-mail as spam or nonspam by comparing it to three other classification algorithms: Ripper, Rocchio, and boosting decision trees. These four algorithms were tested on two different data sets: one data set where the number of features were constrained to the 1000 best features and another data set where the dimensionality was over 7000. SVM performed best when using binary features. For both data sets, boosting trees and SVM had acceptable test performance in terms of accuracy and speed. However, SVM had significantly less training time.
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paper_title: Nonlinear perceptual audio filtering using support vector machines

paper_content:

The perceptually based loss functions for audio filtering used by P.J. Wolfe and S.J. Godsill (see Proc. IEEE ICASSP, vol.2, p.821-4, 2000) are shown to fit well within a complex-valued support vector machine (SVM) framework. SVM regression is extended to the estimation of complex-valued functions, including the derivation of a variant of the sequential minimal optimisation (SMO) algorithm. Audio filters are derived using this, based on an autoregressive (AR) model used for audio and two different Hermitian kernel functions. Results are found to be promising, and further improvements are discussed.
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paper_title: Pattern classification using support vector machine ensemble

paper_content:

While the support vector machine (SVM) can provide a good generalization performance, the classification result of the SVM is often far from the theoretically expected level in practical implementation because they are based on approximated algorithms due to the high complexity of time and space. To improve the limited classification performance of the real SVM, we propose to use an SVM ensemble with bagging (bootstrap aggregating) or boosting. In bagging, each individual SVM is trained independently, using randomly chosen training samples via a bootstrap technique. In boosting, each individual SVM is trained using training samples chosen according to the sample's probability distribution, which is updated in proportion to the degree of error of the sample. In both bagging and boosting, the trained individual SVMs are aggregated to make a collective decision in several ways, such as majority voting, least squares estimation based weighting, and double-layer hierarchical combination. Various simulation results for handwritten digit recognition and fraud detection show that the proposed SVM ensemble with bagging or boosting greatly outperforms a single SVM in terms of classification accuracy.
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paper_title: A support vector machines-based rejection technique for speech recognition

paper_content:

Support vector machines represent a new approach to pattern classification developed from the theory of structural risk minimization. In this paper, we present an investigation into the application of support vector machines to the confidence measurement problem in speech recognition. Specifically, based on the results from an initial decoding of an utterance during speech recognition, we derive a feature vector consisting of parameters such as word score density, N-best word score density differences, relative word score and relative word duration as input to the confidence measurement process in which hypothetically correct utterances are accepted and utterances determined to be incorrect are rejected. We propose a new approach to training support vector machines. In this paper, we train and test a support vector machines classifier and compare the results with other statistical classification methods.
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paper_title: Support vector machine learning for image retrieval

paper_content:

A novel method of relevance feedback is presented based on support vector machine learning in the content-based image retrieval system. A SVM classifier can be learned from training data of relevance images and irrelevance images marked by users. Using the classifier, the system can retrieve more images relevant to the query in the database efficiently. Experiments were carried out on a large-size database of 9918 images. It shows that the interactive learning and retrieval process can find correct images increasingly. It also shows the generalization ability of SVM under the condition of limited training samples.
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paper_title: Financial time-series prediction using least squares support vector machines within the evidence framework

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The Bayesian evidence framework is applied in this paper to least squares support vector machine (LS-SVM) regression in order to infer nonlinear models for predicting a financial time series and the related volatility. On the first level of inference, a statistical framework is related to the LS-SVM formulation which allows one to include the time-varying volatility of the market by an appropriate choice of several hyper-parameters. The hyper-parameters of the model are inferred on the second level of inference. The inferred hyper-parameters, related to the volatility, are used to construct a volatility model within the evidence framework. Model comparison is performed on the third level of inference in order to automatically tune the parameters of the kernel function and to select the relevant inputs. The LS-SVM formulation allows one to derive analytic expressions in the feature space and practical expressions are obtained in the dual space replacing the inner product by the related kernel function using Mercer's theorem. The one step ahead prediction performances obtained on the prediction of the weekly 90-day T-bill rate and the daily DAX30 closing prices show that significant out of sample sign predictions can be made with respect to the Pesaran-Timmerman test statistic.
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paper_title: Cloud classification using support vector machines

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Cloud classification from GOES 8 (Geostationary Operational Environmental Satellite) imagery data is performed using the infrared (IR) channel only. For each block of the image, first and second order statistics are extracted and used to train and test a classifier. In this paper, cloud classification is performed using a support vector machine (SVM) classifier. This scheme, which is typically used to solve two-class problems, has been extended to classify ten different cloud and no-cloud areas. Preliminary results indicate the promise of this method for meteorological applications.
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paper_title: Support Vector Machines for Speaker Verification and Identification

paper_content:

The performance of the support vector machine (SVM) on a speaker verification task is assessed. Since speaker verification requires binary decisions, support vector machines seem to be a promising candidate to perform the task. A new technique for normalising the polynomial kernel is developed and used to achieve performance comparable to other classifiers on the YOHO database. We also present results on a speaker identification task.
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paper_title: Speaker recognition using continuous density support vector machines

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A new classification system for text-independent speaker recognition is presented. This system combines the output probabilities of generative Gaussian mixture models and discriminative classifier support vector machines. The new method is tested on the YOHO database and achieves superior performance.
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paper_title: Support vector machines: relevance feedback and information retrieval

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We compare support vector machines (SVMs) to Rocchio, Ide regular and Ide dec-hi algorithms in information retrieval (IR) of text documents using relevancy feedback. It is assumed a preliminary search finds a set of documents that the user marks as relevant or not and then feedback iterations commence. Particular attention is paid to IR searches where the number of relevant documents in the database is low and the preliminary set of documents used to start the search has few relevant documents. Experiments show that if inverse document frequency (IDF) weighting is not used because one is unwilling to pay the time penalty needed to obtain these features, then SVMs are better whether using term-frequency (TF) or binary weighting. SVM performance is marginally better than Ide dec-hi if TF-IDF weighting is used and there is a reasonable number of relevant documents found in the preliminary search. If the preliminary search is so poor that one has to search through many documents to find at least one relevant document, then SVM is preferred.
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paper_title: Improving Support Vector Machine Classifiers by Modifying Kernel Functions

paper_content:

We propose a method of modifying a kernel function to improve the performance of a support vector machine classifier. This is based on the structure of the Riemannian geometry induced by the kernel function. The idea is to enlarge the spatial resolution around the separating boundary surface, by a conformal mapping, such that the separability between classes is increased. Examples are given specifically for modifying Gaussian Radial Basis Function kernels. Simulation results for both artificial and real data show remarkable improvement of generalization errors, supporting our idea.
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paper_title: Modified support vector machines in financial time series forecasting

paper_content:

This paper proposes a modified version of support vector machines, called C-ascending support vector machine, to model non-stationary financial time series. The C-ascending support vector machines are obtained by a simple modification of the regularized risk function in support vector machines, whereby the recent e-insensitive errors are penalized more heavily than the distant e-insensitive errors. This procedure is based on the prior knowledge that in the non-stationary financial time series the dependency between input variables and output variable gradually changes over the time, specifically, the recent past data could provide more important information than the distant past data. In the experiment, C-ascending support vector machines are tested using three real futures collected from the Chicago Mercantile Market. It is shown that the C-ascending support vector machines with the actually ordered sample data consistently forecast better than the standard support vector machines, with the worst performance when the reversely ordered sample data are used. Furthermore, the C-ascending support vector machines use fewer support vectors than those of the standard support vector machines, resulting in a sparser representation of solution.
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paper_title: Severe storm cell classification using support vector machines and radial basis function approaches

paper_content:

Meteorological volumetric data are used to detect thunderstorms that are the cause of most of the summer severe weathers. There are systems that may convert the volumetric data into a set of derived products. Based on these derived features, this work compares three classifiers to determine which approach will best classify a storm cell data set coming from Environment Canada. The criterion for comparison is the accuracy in the classification over a testing set. The three approaches compared are the support vector machine (SVM) classifier, with radial basis function (RBF) kernel; the classic RBF classifier, with the centres found using the orthogonal least squares approach; and the hybrid RBF, with the centres corresponding to the support vectors found using the SVM approach. The results show that the SVM approach is the best of these approaches, in terms of accuracy, for the storm cell classification.
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paper_title: Text categorization with support vector machines: Learning with many relevant features

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This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of different learning tasks. Furthermore they are fully automatic, eliminating the need for manual parameter tuning.
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paper_title: Automatic text location in images and video frames

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Automatic text location (without character recognition capabilities) deals with extracting image regions that contain text only. The images of these regions can then be fed to an optical character recognition module or highlighted for users. This is very useful in a number of applications such as database indexing and converting paper documents to their electronic versions. The performance of our automatic text location algorithm is shown in several applications. Compared with some traditional text location methods, our method has the following advantages: 1) low computational cost; 2) robust to font size; and 3) high accuracy.
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paper_title: Update relevant image weights for content-based image retrieval using support vector machines

paper_content:

Relevance feedback (Y. Rui et al., 1998) has been a powerful tool for interactive content based image retrieval (CBIR). During the retrieval process, the user selects the most relevant images and provides a weight of preference for each relevant image. The user's high level query and perception subjectivity can be captured to some extent by dynamically updated low-level feature weights based on the user's feedback. However, in MARS (Y. Rui et al., 1997), only the positive feedbacks, i.e., relevant images are considered. A novel approach is proposed by providing both positive and negative feedbacks for support vector machine (SVM) learning. The SVM learning results are used to update the weights of preference for relevant images. Priorities are given to the positive feedbacks that have larger distances to the hyperplane determined by the support vectors. This approach releases the user from manually providing preference weight for each positive example, i.e., relevant image as before. Experimental results show that the proposed approach has reasonable improvement over relevance feedback with possible examples only.
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paper_title: Goal detection in football by using support vector machines for classification

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We present a technique for detecting goals during a football match by using images acquired by a single camera placed externally to the field. The method does not require the modification neither of the ball nor of the goalmouth. Due to the attitude of the camera with respect to the football ground, the system can be thought of as an electronic linesman which helps the referee in establishing the occurrence of a goal during a football match. The occurrence of the event is established by detecting the ball and comparing its position with respect to the location of the goalpost in the image. The ball detection technique relies on a supervised learning scheme called support vector machines for classification. The examples used for training are appropriately filtered version of views of the object to be detected, previously stored in the form of image patterns. We have extensively tested the technique on real images in which the ball is both fully visible and partially occluded. The performance of the proposed detection scheme are measured in terms of detection rate, false positive rate and precision in the ball localisation in image.
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paper_title: Gender classification using support vector machines

paper_content:

In this paper, support vector machines (SVMs) are investigated for visual gender classification with low-resolution "thumbnail" faces (21-by-12 pixels) processed from 1,755 images from the FERET face database. The performance of SVMs (3.4% error) is shown to be superior to traditional pattern classifiers (linear, quadratic, Fisher linear discriminant, nearest neighbor) as well as more modern techniques such as radial basis function (RBF) classifiers and large ensemble-RBF networks. SVMs have also been tested with high-resolution (80-by-40 pixels) images. The difference between low and high-resolution inputs with SVMs was only 1%, thus demonstrating a degree of robustness and relative scale invariance.
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paper_title: Classification of hyperspectral data using support vector machine

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Classification is one of the most important tasks for remote sensing image processing. Most of the existing supervised classification methods are based on traditional statistics, which can provide ideal results when sample size is tending to infinity. However, only finite samples can be acquired in practice. In addition, many methods are constrained by high data dimension of hyperspectral images. In this paper, a novel learning method, the support vector machine (SVM), is applied to hyperspectral data classification. This method does not suffer the limitations of data dimensionality and limited samples. The foundations of the SVM have been developed by Vapnik (1995) and are gaining popularity in field of machine learning due to many attractive features and promising empirical performance. In our experiment, the support vectors, which are critical for classification, are obtained by learning from the training samples. Choosing appropriate kernel function and suitable parameters, better classification results are obtained.
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paper_title: Support Vector Learning for Gender Classification Using Audio and Visual Cues: A Comparison

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Computer vision systems for monitoring people and collecting valuable demographics in a social environment will play an increasingly important role in enhancing user's experience and can significantly improve the intelligibility of a human computer interaction (HCI) system. For example, a robust gender classification system is expected to provide a basis for passive surveillance and access to a smart building using demographic information or can provide valuable consumer statistics in a public place. The option of an audio cue in addition to the visual cue promises a robust solution with high accuracy and ease-of-use in human computer interaction systems.This paper investigates the use of Support Vector Machines(SVMs) for the purpose of gender classification. Both visual (thumbnail frontal face) and audio (features from speech data) cues were considered for designing the classifier and the performance obtained by using each cue was compared. The performance of the SVM was compared with that of two simple classifiers namely, the nearest prototype neighbor and the k-nearest neighbor on all feature sets. It was found that the SVM outperformed the other two classifiers on all datasets. The best overall classification rates obtained using the SVM for the visual and speech data were 95.31% and 100%, respectively.
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paper_title: Pose classification using support vector machines

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In this work a software architecture is presented for the automatic recognition of human arm poses. Our research has been carried on in the robotics framework. A mobile robot that has to find its path to the goal in a partially structured environment can be trained by a human operator to follow particular routes in order to perform its task quickly. The system is able to recognize and classify some different poses of the operator's arms as direction commands like "turn-left", "turn-right", "go-straight", and so on. A binary image of the operator silhouette is obtained from the gray-level input. Next, a slice centered on the silhouette itself is processed in order to compute the eigenvalues vector of the pixels covariance matrix. Finally, a support vector machine is trained to classify different poses using the eigenvalues array. A detailed description of the system is presented. Experimental results and an outline of the usability of the system as a generic shape classification tool are reported.
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paper_title: Image classification by support vector machines

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Support vector machines are used to classify plane targets on a binary remote sensing. The experimental results have shown that support vector machines can generalize well on difficult image classification problems where the only features are high dimensional binary values. Moreover, we observed that the range of the binary values of images affect the performance of classification results.
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paper_title: Automatic text extraction in news images using morphology

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In this paper we present a new method to extract both superimposed and embedded graphical texts in a freeze-frame of news video. The algorithm is summarized in the following three steps. For the first step, we convert a color image into a gray-level image and apply contrast stretching to enhance the contrast of the input image. Then, a modified local adaptive thresholding is applied to the contrast-stretched image. The second step is divided into three processes: eliminating text-like components by applying erosion, dilation, and (OpenClose + CloseOpen)/2 morphological operations, maintaining text components using (OpenClose + CloseOpen)/2 operation with a new Geo-correction method, and subtracting two result images for eliminating false-positive components further. In the third filtering step, the characteristics of each component such as the ratio of the number of pixels in each candidate component to the number of its boundary pixels and the ratio of the minor to the major axis of each bounding box are used. Acceptable results have been obtained using the proposed method on 300 news images with a recognition rate of 93.6%. Also, our method indicates a good performance on all the various kinds of images by adjusting the size of the structuring element.
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paper_title: Visual speech recognition using support vector machines

paper_content:

In this paper we propose a visual speech recognition network based on support vector machines. Each word of the dictionary is described as a temporal sequence of visemes. Each viseme is described by a support vector machine, and the temporal character of speech is modeled by integrating the support vector machines as nodes into a Viterbi decoding lattice. Experiments conducted on a small visual speech recognition task show a word recognition rate on the level of the best rates previously reported, even without training the state transition probabilities in the Viterbi lattice and using very simple features. This proves the suitability of support vector machines for visual speech recognition.
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paper_title: Incorporate support vector machines to content-based image retrieval with relevance feedback

paper_content:

By using relevance feedback, content-based image retrieval (CBIR) allows the user to retrieve images interactively. Beginning with a coarse query, the user can select the most relevant images and provide a weight of preference for each relevant image to refine the query. The high level concept borne by the user and perception subjectivity of the user can be automatically captured by the system to some degree. This paper proposes an approach to utilize both positive and negative feedbacks for image retrieval. Support vector machines (SVM) is applied to classifying the positive and negative images. The SVM learning results are used to update the preference weights for the relevant images. This approach releases the user from manually providing preference weight for each positive example. Experimental results show that the proposed approach has improvement over the previous approach (Rui et al. 1997) that uses positive examples only.
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paper_title: Probability prediction using support vector machines

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Reports on an investigation of the use of support vector machines in the problem of beating bookmakers' odds. The method used is to create nine regression lines, one for each of the possible half-time/full-time possibilities, e.g. a draw at half time with the home team winning at full time. We extend the support vector machine by incorporating variable-width permissible error bounds based on our knowledge of the sample size on which each of the data points is based. We show that the variable-width method improves the regression.
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paper_title: Combining flat and structured representations for fingerprint classification with recursive neural networks and support vector machines

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We present new fingerprint classification algorithms based on two machine learning approaches: support vector machines (SVMs) and recursive neural networks (RNNs). RNNs are trained on a structured representation of the fingerprint image. They are also used to extract a set of distributed features of the fingerprint which can be integrated in the SVM. SVMs are combined with a new error-correcting code scheme. This approach has two main advantages: (a) It can tolerate the presence of ambiguous fingerprint images in the training set and (b) it can effectively identify the most difficult fingerprint images in the test set. By rejecting these images the accuracy of the system improves significantly. We report experiments on the fingerprint database NIST-4. Our best classification accuracy is of 95.6 percent at 20 percent rejection rate and is obtained by training SVMs on both FingerCode and RNN-extracted features. This result indicates the benefit of integrating global and structured representations and suggests that SVMs are a promising approach for fingerprint classification.
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paper_title: Bullet-hole image classification with support vector machines

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This paper focuses on the application of support vector machines (SVM) for classification of bullet hole images in an auto-scoring system. In order to automatically calculate the score of a shooter, the bullet-hole images can be classified as one, two or more bullet-hole images. For the auto-scoring system, two main issues are considered. One is to extract important features of bullet-hole images from the target paper; the other is to classify these images into correct classes. A set of essential features of bullet-hole images are discussed and used for the subsequent classification. SVM has been applied to the multi-class classification problem. Experimental results show that both the extracted features and SV learning algorithms are effective and efficient for the project.
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paper_title: Learning the decision function for speaker verification

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Explores the possibility of replacing the usual thresholding decision rule of log likelihood ratios used in speaker verification systems by more complex and discriminant decision functions based for instance on linear regression models or support vector machines. Current speaker verification systems, based on generative models such as HMMs or Gaussian mixture models, can indeed easily be adapted to use such decision functions. Experiments on both text dependent and text independent tasks always yielded performance improvements and sometimes significantly.
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paper_title: Learning gender with support faces

paper_content:

Nonlinear support vector machines (SVMs) are investigated for appearance-based gender classification with low-resolution "thumbnail" faces processed from 1,755 images from the FERET (FacE REcognition Technology) face database. The performance of SVMs (3.4% error) is shown to be superior to traditional pattern classifiers (linear, quadratic, Fisher linear discriminant, nearest-neighbor) as well as more modern techniques, such as radial basis function (RBF) classifiers and large ensemble-RBF networks. Furthermore, the difference in classification performance with low-resolution "thumbnails" (21/spl times/12 pixels) and the corresponding higher-resolution images (84/spl times/48 pixels) was found to be only 1%, thus demonstrating robustness and stability with respect to scale and the degree of facial detail.
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paper_title: Selecting bankruptcy predictors using a support vector machine approach

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The conventional neural network approach has been found useful in predicting corporate distress from financial statements. We have adapted a support vector machine approach to the problem. A way of selecting bankruptcy predictors is shown, using the Euclidean distance based criterion calculated within the SVM kernel. A comparative study is provided using three classical corporate distress models and an alternative model based on the SVM approach.
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paper_title: Data condensation in large databases by incremental learning with support vector machines

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An algorithm for data condensation using support vector machines (SVM) is presented. The algorithm extracts data points lying close to the class boundaries, which form a much reduced but critical set for classification. The problem of large memory requirements for training SVM in batch mode is circumvented by adopting an active incremental learning algorithm. The learning strategy is motivated from the condensed nearest neighbor classification technique. Experimental results presented show that such active incremental learning enjoy superiority in terms of computation time and condensation ratio, over related methods.
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paper_title: A subspace approach to face detection with support vector machines

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We present a subspace approach to face detection with support vector machines (SVMs). A linear SVM classifier is trained as a filter to produce a subspace in which a non-linear SVM classifier with Gaussian kernel is trained for face detection. This makes training easier and results in a very efficient face detection algorithm. Experimental results demonstrate their promising performance compared with some well-known existing detectors.
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paper_title: Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines

paper_content:

This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possible QP problems. These small QP problems are solved analytically, which avoids using a time-consuming numerical QP optimization as an inner loop. The amount of memory required for SMO is linear in the training set size, which allows SMO to handle very large training sets. Because matrix computation is avoided, SMO scales somewhere between linear and quadratic in the training set size for various test problems, while the standard chunking SVM algorithm scales somewhere between linear and cubic in the training set size. SMO’s computation time is dominated by SVM evaluation, hence SMO is fastest for linear SVMs and sparse data sets. On realworld sparse data sets, SMO can be more than 1000 times faster than the chunking algorithm.
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paper_title: Multi-modal identity verification using support vector machines (SVM)

paper_content:

The contribution of this paper is twofold: (1) to formulate a decision fusion problem that is encountered in the design of a multi-modal identity verification system as a particular classification problem, and (2) to solve this problem by using a support vector machine (SVM). The multi-modal identity verification system under consideration is built of d modalities in parallel, each one delivering as output a scalar number, called a score, stating how well the claimed identity is verified. A fusion module receiving the d scores as input has to take a binary decision: to accept or reject the identity. This fusion problem has been solved using SVMs. The performance of this fusion module has been evaluated and compared with other proposed methods on a multi-modal database containing both vocal and visual modalities.
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paper_title: Improvements to Platt's SMO Algorithm for SVM Classifier Design

paper_content:

This article points out an important source of inefficiency in Platt's sequential minimal optimization (SMO) algorithm that is caused by the use of a single threshold value. Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO. These modified algorithms perform significantly faster than the original SMO on all benchmark data sets tried.
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paper_title: Modified support vector machines in financial time series forecasting

paper_content:

This paper proposes a modified version of support vector machines, called C-ascending support vector machine, to model non-stationary financial time series. The C-ascending support vector machines are obtained by a simple modification of the regularized risk function in support vector machines, whereby the recent e-insensitive errors are penalized more heavily than the distant e-insensitive errors. This procedure is based on the prior knowledge that in the non-stationary financial time series the dependency between input variables and output variable gradually changes over the time, specifically, the recent past data could provide more important information than the distant past data. In the experiment, C-ascending support vector machines are tested using three real futures collected from the Chicago Mercantile Market. It is shown that the C-ascending support vector machines with the actually ordered sample data consistently forecast better than the standard support vector machines, with the worst performance when the reversely ordered sample data are used. Furthermore, the C-ascending support vector machines use fewer support vectors than those of the standard support vector machines, resulting in a sparser representation of solution.
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paper_title: Multi-modal identity verification using support vector machines (SVM)

paper_content:

The contribution of this paper is twofold: (1) to formulate a decision fusion problem that is encountered in the design of a multi-modal identity verification system as a particular classification problem, and (2) to solve this problem by using a support vector machine (SVM). The multi-modal identity verification system under consideration is built of d modalities in parallel, each one delivering as output a scalar number, called a score, stating how well the claimed identity is verified. A fusion module receiving the d scores as input has to take a binary decision: to accept or reject the identity. This fusion problem has been solved using SVMs. The performance of this fusion module has been evaluated and compared with other proposed methods on a multi-modal database containing both vocal and visual modalities.
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paper_title: A Tutorial on Support Vector Machines for Pattern Recognition

paper_content:

The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.
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paper_title: The Nature of Statistical Learning Theory

paper_content:

Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
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paper_title: Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines

paper_content:

This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possible QP problems. These small QP problems are solved analytically, which avoids using a time-consuming numerical QP optimization as an inner loop. The amount of memory required for SMO is linear in the training set size, which allows SMO to handle very large training sets. Because matrix computation is avoided, SMO scales somewhere between linear and quadratic in the training set size for various test problems, while the standard chunking SVM algorithm scales somewhere between linear and cubic in the training set size. SMO’s computation time is dominated by SVM evaluation, hence SMO is fastest for linear SVMs and sparse data sets. On realworld sparse data sets, SMO can be more than 1000 times faster than the chunking algorithm.
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paper_title: Improvements to Platt's SMO Algorithm for SVM Classifier Design

paper_content:

This article points out an important source of inefficiency in Platt's sequential minimal optimization (SMO) algorithm that is caused by the use of a single threshold value. Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO. These modified algorithms perform significantly faster than the original SMO on all benchmark data sets tried.
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paper_title: Support Vector Machines for 3D Object Recognition

paper_content:

Support vector machines (SVMs) have been recently proposed as a new technique for pattern recognition. Intuitively, given a set of points which belong to either of two classes, a linear SVM finds the hyperplane leaving the largest possible fraction of points of the same class on the same side, while maximizing the distance of either class from the hyperplane. The hyperplane is determined by a subset of the points of the two classes, named support vectors, and has a number of interesting theoretical properties. In this paper, we use linear SVMs for 3D object recognition. We illustrate the potential of SVMs on a database of 7200 images of 100 different objects. The proposed system does not require feature extraction and performs recognition on images regarded as points of a space of high dimension without estimating pose. The excellent recognition rates achieved in all the performed experiments indicate that SVMs are well-suited for aspect-based recognition.
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paper_title: Large margin dags for multiclass classification

paper_content:

We present a new learning architecture: the Decision Directed Acyclic Graph (DDAG), which is used to combine many two-class classifiers into a multiclass classifier. For an N-class problem, the DDAG contains N(N - 1)/2 classifiers, one for each pair of classes. We present a VC analysis of the case when the node classifiers are hyperplanes; the resulting bound on the test error depends on N and on the margin achieved at the nodes, but not on the dimension of the space. This motivates an algorithm, DAGSVM, which operates in a kernel-induced feature space and uses two-class maximal margin hyperplanes at each decision-node of the DDAG. The DAGSVM is substantially faster to train and evaluate than either the standard algorithm or Max Wins, while maintaining comparable accuracy to both of these algorithms.
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paper_title: Face recognition with support vector machines: global versus component-based approach

paper_content:

We present a component-based method and two global methods for face recognition and evaluate them with respect to robustness against pose changes. In the component system we first locate facial components, extract them and combine them into a single feature vector which is classified by a Support Vector Machine (SVM). The two global systems recognize faces by classifying a single feature vector consisting of the gray values of the whole face image. In the first global system we trained a single SVM classifier for each person in the database. The second system consists of sets of viewpoint-specific SVM classifiers and involves clustering during training. We performed extensive tests on a database which included faces rotated up to about 40/spl deg/ in depth. The component system clearly outperformed both global systems on all tests.
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paper_title: Face detection and facial feature extraction using support vector machines

paper_content:

Proposes a fast algorithm for detecting human face and extracting the facial features. For this task, we have developed a flexible coordinate system and several support vector machines. The design of a face model for both detection and extraction is based on multi-resolution wavelet decomposition (MWD). Using a mean face, the MWD and a small number of feature points are applied for rough searching by estimating the modified cross correlation (MCC). More accurate results can be achieved by a serious of support vector machines (SVMs). Experimental results show that the proposed approach is fast and has a high detection rate even in cases when a face is embedded in a complicated background.
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paper_title: Face Detection Based on Cost-Sensitive Support Vector Machines

paper_content:

This paper presents a method of detecting faces based on cost-sensitive Support Vector Machines (SVM). In our method, different costs are given to the misclassification of having a face missed and having a false alarm to train the SVM classifiers. The method achieves significant speed-ups over conventional SVM-based methods without reducing detection rate too much and the hierarchical architecture of the detector also reduces the complexity of training of the nonlinear SVM classifier. Experimental results have demonstrated the effectiveness of the method.
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paper_title: Invariant face detection with support vector machines

paper_content:

This paper present an analysis of the performance of support vector machines (SVMs) for automatic detection of human faces in static color images of complex scenes. Skin color-based image segmentations initially performed for several different chrominance spaces by use of the single Gaussian chrominance model and a Gaussian mixture density model. Feature extraction in the segmented images is then implemented by use of invariant orthogonal Fourier-Mellin moments. For all chrominance spaces, the application of SVMs to the invariant moments obtained from a set of 100 test images yields a higher face detection performance than when applying a 3-layer perceptron neural network (NN), depending on a suitable selection of the kernel function used to train the SVM and of the value of its associated parameter(s). The training of SVMs is easier and faster than that of a NN, always finds a global minimum, and SVMs have a better generalization ability.
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paper_title: Face Detection Based on Support Vector Machines

paper_content:

Face detection is a key problem in building an automatic face system such as face recognition and authentication. A number of approaches have been proposed for face detection. Recently, a novel statistical machine learning method, support vector machine, has been employed. Generally, the current SVM-based methods can be divided into two categories: component-based and whole face-based. It is difficult for the component-based method to extract the small face due to no enough information for each component exists. On the other hand, the whole face-based method is too much computationally expensive to build an effective system. In this paper we present a fast system named wavelet-SVM method to extract a wide range scales of faces from grey-scale images or color images with a preprocessing using a TSL color model. The system is not only accurate and effective, but also largely speeds the system up by applying a TSL B-G color model and multiresolution wavelet decomposition.
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paper_title: Recognition of facial images using support vector machines

paper_content:

A novel support vector machine (SVM)-based method for appearance-based face recognition is presented. The proposed method does not use any external feature extraction process. Accordingly the intensities of the raw pixels that make up the face pattern are fed directly to the SVM. However, it takes account of prior knowledge about facial structures in the form of a kernel embedded in the SVM architecture. The new kernel efficiently explores spatial relationships among potential eye, nose, and mouth objects and is compared with existing kernels. Experiments with the ORL database show a recognition rate of 98% and speed of 0.22 seconds per face with 40 classes.
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paper_title: Composite support vector machines for detection of faces across views and pose estimation

paper_content:

Abstract Support vector machines (SVMs) have shown great potential for learning classification functions that can be applied to object recognition. In this work, we extend SVMs to model the appearance of human faces which undergo non-linear change across multiple views. The approach uses inherent factors in the nature of the input images and the SVM classification algorithm to perform both multi-view face detection and pose estimation.
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paper_title: On the stability of support vector machines for face detection

paper_content:

In this paper we study the stability of support vector machines in face detection by decomposing their average prediction error into the bias, variance, and aggregation effect terms. Such an analysis indicates whether bagging, a method for generating multiple versions of a classifier from bootstrap samples of a training set, and combining their outcomes by majority voting, is expected to improve the accuracy of the classifier. We estimate the bias, variance, and aggregation effect by using bootstrap smoothing techniques when support vector machines are applied to face detection in the AT & T face database and we demonstrate that support vector machines are stable classifiers. Accordingly, bagging is not expected to improve their face detection accuracy.
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paper_title: Face recognition with support vector machines: global versus component-based approach

paper_content:

We present a component-based method and two global methods for face recognition and evaluate them with respect to robustness against pose changes. In the component system we first locate facial components, extract them and combine them into a single feature vector which is classified by a Support Vector Machine (SVM). The two global systems recognize faces by classifying a single feature vector consisting of the gray values of the whole face image. In the first global system we trained a single SVM classifier for each person in the database. The second system consists of sets of viewpoint-specific SVM classifiers and involves clustering during training. We performed extensive tests on a database which included faces rotated up to about 40/spl deg/ in depth. The component system clearly outperformed both global systems on all tests.
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paper_title: A general framework for object detection

paper_content:

This paper presents a general trainable framework for object detection in static images of cluttered scenes. The detection technique we develop is based on a wavelet representation of an object class derived from a statistical analysis of the class instances. By learning an object class in terms of a subset of an overcomplete dictionary of wavelet basis functions, we derive a compact representation of an object class which is used as an input to a support vector machine classifier. This representation overcomes both the problem of in-class variability and provides a low false detection rate in unconstrained environments. We demonstrate the capabilities of the technique in two domains whose inherent information content differs significantly. The first system is face detection and the second is the domain of people which, in contrast to faces, vary greatly in color, texture, and patterns. Unlike previous approaches, this system learns from examples and does not rely on any a priori (hand-crafted) models or motion-based segmentation. The paper also presents a motion-based extension to enhance the performance of the detection algorithm over video sequences. The results presented here suggest that this architecture may well be quite general.
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paper_title: Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication

paper_content:

A novel method for enhancing the performance of elastic graph matching in frontal face authentication is proposed. The starting point is to weigh the local similarity values at the nodes of an elastic graph according to their discriminatory power. Powerful and well-established optimization techniques are used to derive the weights of the linear combination. More specifically, we propose a novel approach that reformulates Fisher's discriminant ratio to a quadratic optimization problem subject to a set of inequality constraints by combining statistical pattern recognition and support vector machines (SVM). Both linear and nonlinear SVM are then constructed to yield the optimal separating hyperplanes and the optimal polynomial decision surfaces, respectively. The method has been applied to frontal face authentication on the M2VTS database. Experimental results indicate that the performance of morphological elastic graph matching is highly improved by using the proposed weighting technique.
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paper_title: Face recognition using component-based svm classification and morphable models

paper_content:

We present a novel approach to pose and illumination invariant face recognition that combines two recent advances in the computer vision field: component-based recognition and 3D morphable models. In a first step a 3D morphable model is used to generate 3D face models from only two input images from each person in the training database. By rendering the 3D models under varying pose and illumination conditions we then create a vast number of synthetic face images which are used to train a component-based face recognition system. In preliminary experiments we show the potential of our approach regarding pose and illumination invariance.
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paper_title: Face recognition by support vector machines

paper_content:

Support vector machines (SVM) have been recently proposed as a new technique for pattern recognition. SVM with a binary tree recognition strategy are used to tackle the face recognition problem. We illustrate the potential of SVM on the Cambridge ORL face database, which consists of 400 images of 40 individuals, containing quite a high degree of variability in expression, pose, and facial details. We also present the recognition experiment on a larger face database of 1079 images of 137 individuals. We compare the SVM-based recognition with the standard eigenface approach using the nearest center classification (NCC) criterion.
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paper_title: Support vector machines for face recognition

paper_content:

Abstract Support vector machines (SVMs) have been recently proposed as a new learning network for bipartite pattern recognition. In this paper, SVMs incorporated with a binary tree recognition strategy are proposed to tackle the multi-class face recognition problem. The binary tree extends naturally, the pairwise discrimination capability of the SVMs to the multi-class scenario. Two face databases are used to evaluate the proposed method. The performance of the SVMs based face recognition is compared with the standard eigenface approach, and also the more recently proposed algorithm called the nearest feature line (NFL).
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paper_title: Support vector regression and classification based multi-view face detection and recognition

paper_content:

A support vector machine-based multi-view face detection and recognition framework is described. Face detection is carried out by constructing several detectors, each of them in charge of one specific view. The symmetrical property of face images is employed to simplify the complexity of the modelling. The estimation of head pose, which is achieved by using the support vector regression technique, provides crucial information for choosing the appropriate face detector. This helps to improve the accuracy and reduce the computation in multi-view face detection compared to other methods. For video sequences, further computational reduction can be achieved by using a pose change smoothing strategy. When face detectors find a face in frontal view, a support vector machine-based multi-class classifier is activated for face recognition. All the above issues are integrated under a support vector machine framework. Test results on four video sequences are presented, among them the detection rate is above 95%, recognition accuracy is above 90%, average pose estimation error is around 10/spl deg/, and the full detection and recognition speed is up to 4 frames/second on a Pentium II 300 PC.
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paper_title: Multi-view face detection using support vector machines and eigenspace modelling

paper_content:

An approach to multi-view face detection based on head pose estimation is presented in this paper. Support vector regression is employed to solve the problem of pose estimation. Three methods, the eigenface method the support vector machine (SVM) based method, and a combination of the two methods, are investigated. The eigenface method, which seeks to estimate the overall probability distribution of patterns to be recognised, is fast but less accurate because of the overlap of confidence distributions between face and non-face classes. On the other hand, the SVM method, which tries to model the boundary of two classes to be classified is more accurate but slower as the number of support vectors is normally large. The combined method can achieve an improved performance by speeding up the computation and keeping the accuracy to a preset level. It can be used to automatically detect and track faces in face verification and identification systems.
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paper_title: Facial feature detection and face recognition from 2D and 3D images

paper_content:

Abstract This paper presents a feature-based face recognition system based on both 3D range data as well as 2D gray-level facial images. Feature points are described by Gabor filter responses in the 2D domain and Point Signature in the 3D domain. Extracted shape features from 3D feature points and texture features from 2D feature points are first projected into their own subspace using PCA. In subspace, the corresponding normalized shape and texture weight vectors are then integrated to form an augmented vector which is used to represent each facial image. For a given test facial image, the best match in the model library is identified according to similarity function or Support Vector Machine (SVM). Experimental results involving 50 persons with different facial expressions and extracted from different viewpoints have demonstrated the efficiency of our algorithm.
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paper_title: Multi-view face detection and pose estimation using a composite support vector machine across the view sphere

paper_content:

Support vector machines have shown great potential for learning classification functions that can be applied to object recognition. In this work, we extend SVMs to model the 2D appearance of human faces which undergo nonlinear change across the view sphere. The model enables simultaneous multi-view face detection and pose estimation at near-frame rate.
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paper_title: SVM-based detection of moving vehicles for automatic traffic monitoring

paper_content:

A traffic surveillant system must be capable of working in all kinds of weather and illumination conditions, such as shadows in a sunny day, vehicle reflections in a rainy day and vehicle headlights in the evening. In this paper we propose a robust algorithm to detect real moving vehicles and eliminate the influence of shadows and vehicle headlights by using a pattern classification method. On account of its simple but efficient representation, the histogram of a difference image is used as the features for classification. The classifier is designed based on support vector machine (SVM) due to its high generalization performance. The final experiment shows that the real traffic monitoring system based on our algorithm can detect moving vehicles and separate shadows and headlights robustly and effectively in different weather and illumination conditions.
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paper_title: Real-time pedestrian detection using support vector machines

paper_content:

In this paper, we present a real-time pedestrian detection method in outdoor environments. It is necessary for pedestrian detection to implement obstacle and face detection which are major parts of a walking guidance system for the visually impaired. It detects foreground objects on the ground, discriminates pedestrians from other noninterest objects, and extracts candidate regions for face detection and recognition. For effective real-time pedestrian detection, we have developed a method using stereo-based segmentation and the SVM (Support Vector Machines), which works well particularly in binary classification problem (e.g. object detection). We used vertical edge features extracted from arms, legs and torso. In our experiments, test results on a large number of outdoor scenes demonstrated the effectiveness of the proposed pedestrian detection method.
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paper_title: People recognition and pose estimation in image sequences

paper_content:

Presents a system which learns from examples to automatically recognize people and estimate their poses in image sequences with the potential application to daily surveillance in indoor environments. The person in the image is represented by a set of features based on color and shape information. Recognition is carried out through a hierarchy of biclass SVM classifiers that are separately trained to recognize people and estimate their poses. The system shows a very high accuracy in people recognition and about 85% level of performance in pose estimation, outperforming in both cases k-nearest neighbors classifiers. The system works in real time.
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paper_title: Radar target recognition based on support vector machine

paper_content:

Support vector machine is a new kind technique for pattern recognition. We study the classification mechanism of SVM in detail in this paper. Finally, the simulations are done according to SVM algorithm and the results show the advantage of it.
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paper_title: Representing and recognizing visual dynamic events with support vector machines

paper_content:

Support vector machines (SVM) have been recently introduced as techniques for solving pattern recognition and regression estimation problems. SVM are derived within the framework of statistical learning theory and combine a solid theoretical foundation with very good performances in several applications. In this paper we describe a system able to detect, represent, and recognize visual dynamic events from an image sequence. While the events are initially detected by means of low-level visual processing, both the representation and recognition stages are performed with SVM. Therefore, the system is trained, instead of programmed, to perform the required tasks. The very good results obtained on real image sequences indicate that SVM can be profitably used for the construction of flexible and effective systems based on computer vision.
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paper_title: View-based 3D object recognition with support vector machines

paper_content:

Support vector machines have demonstrated excellent results in pattern recognition tasks and 3D object recognition. We confirm some of the results in 3D object recognition and compare it to other object recognition systems. We use different pixel-level representations to perform the experiments, while we extend the setting to the more challenging and practical case when only a limited number of views of the object are presented during training. We report high correct classification of unseen views, especially considering that no domain knowledge is including into the proposed system. Finally, we suggest an active learning algorithm to reduce further the required number of training views.
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paper_title: Support Vector Machines for 3D Object Recognition

paper_content:

Support vector machines (SVMs) have been recently proposed as a new technique for pattern recognition. Intuitively, given a set of points which belong to either of two classes, a linear SVM finds the hyperplane leaving the largest possible fraction of points of the same class on the same side, while maximizing the distance of either class from the hyperplane. The hyperplane is determined by a subset of the points of the two classes, named support vectors, and has a number of interesting theoretical properties. In this paper, we use linear SVMs for 3D object recognition. We illustrate the potential of SVMs on a database of 7200 images of 100 different objects. The proposed system does not require feature extraction and performs recognition on images regarded as points of a space of high dimension without estimating pose. The excellent recognition rates achieved in all the performed experiments indicate that SVMs are well-suited for aspect-based recognition.
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paper_title: Handwritten digit recognition by combining support vector machines using rule-based reasoning

paper_content:

The idea of combining classifiers in order to compensate their individual weakness and to preserve their individual strength has been widely used in pattern recognition applications. The cooperation of two feature families for handwritten digit recognition using SVM (Support Vector Machine) classifiers is examined. We investigate the advantages and weaknesses of various decision fusion schemes using rule-based reasoning. The obtained results show that it is difficult to exceed the recognition rate of the classifier applied straightforwardly on the feature families as one set. However, the rule-based cooperation schemes enable an easy and efficient implementation of various rejection criteria that leads to high reliability recognition systems.
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paper_title: Robust vision-based features and classification schemes for off-line handwritten digit recognition

paper_content:

Abstract We use well-established results in biological vision to construct a model for handwritten digit recognition. We show empirically that the features extracted by our model are linearly separable over a large training set (MNIST). Using only a linear discriminant system on these features, our model is relatively simple yet outperforms other models on the same data set. In particular, the best result is obtained by applying triowise linear support vector machines with soft voting on vision-based features extracted from deslanted images.
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paper_title: Handprinted Hiragana recognition using support vector machines

paper_content:

Describes a method to improve the cumulative recognition rates of pattern recognition using a decision directed acyclic graph (DDAG) based on support vector machines (SVM). Though the original DDAG has high level of performance and its execution speed is fast, it does not consider the so-called cumulative recognition rate. We construct a DDAG which can incorporate the cumulative recognition rate. As a result of our experiment for handprinted Hiragana characters in JEITA-HP, the cumulative recognition rate is improved and its execution time is almost the same as the original DDAG and 30 times faster than the Max Win Algorithm which is one of the famous recognition methods using support vector machines for a multi-class problem.
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paper_title: A support vector machines-based rejection technique for speech recognition

paper_content:

Support vector machines represent a new approach to pattern classification developed from the theory of structural risk minimization. In this paper, we present an investigation into the application of support vector machines to the confidence measurement problem in speech recognition. Specifically, based on the results from an initial decoding of an utterance during speech recognition, we derive a feature vector consisting of parameters such as word score density, N-best word score density differences, relative word score and relative word duration as input to the confidence measurement process in which hypothetically correct utterances are accepted and utterances determined to be incorrect are rejected. We propose a new approach to training support vector machines. In this paper, we train and test a support vector machines classifier and compare the results with other statistical classification methods.
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paper_title: Application of support vector machines classifiers to visual speech recognition

paper_content:

In this paper we propose a visual speech recognition network based on support vector machines. Each word of the dictionary is modeled by a set of temporal sequences of visemes. Each viseme is described by a support vector machine, and the temporal character of speech is modeled by integrating the support vector machines as nodes into a Viterbi decoding lattice. Experiments conducted on a small visual speech recognition task using very simple features demonstrate a word recognition rate on the level of the best rates previously reported even without training the state transition probabilities in the Viterbi lattices. This proves the suitability of support vector machines for visual speech recognition.
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paper_title: Speaker recognition using continuous density support vector machines

paper_content:

A new classification system for text-independent speaker recognition is presented. This system combines the output probabilities of generative Gaussian mixture models and discriminative classifier support vector machines. The new method is tested on the YOHO database and achieves superior performance.
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paper_title: Visual speech recognition using support vector machines

paper_content:

In this paper we propose a visual speech recognition network based on support vector machines. Each word of the dictionary is described as a temporal sequence of visemes. Each viseme is described by a support vector machine, and the temporal character of speech is modeled by integrating the support vector machines as nodes into a Viterbi decoding lattice. Experiments conducted on a small visual speech recognition task show a word recognition rate on the level of the best rates previously reported, even without training the state transition probabilities in the Viterbi lattice and using very simple features. This proves the suitability of support vector machines for visual speech recognition.
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paper_title: Support vector machine learning for image retrieval

paper_content:

A novel method of relevance feedback is presented based on support vector machine learning in the content-based image retrieval system. A SVM classifier can be learned from training data of relevance images and irrelevance images marked by users. Using the classifier, the system can retrieve more images relevant to the query in the database efficiently. Experiments were carried out on a large-size database of 9918 images. It shows that the interactive learning and retrieval process can find correct images increasingly. It also shows the generalization ability of SVM under the condition of limited training samples.
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paper_title: Support vector machines: relevance feedback and information retrieval

paper_content:

We compare support vector machines (SVMs) to Rocchio, Ide regular and Ide dec-hi algorithms in information retrieval (IR) of text documents using relevancy feedback. It is assumed a preliminary search finds a set of documents that the user marks as relevant or not and then feedback iterations commence. Particular attention is paid to IR searches where the number of relevant documents in the database is low and the preliminary set of documents used to start the search has few relevant documents. Experiments show that if inverse document frequency (IDF) weighting is not used because one is unwilling to pay the time penalty needed to obtain these features, then SVMs are better whether using term-frequency (TF) or binary weighting. SVM performance is marginally better than Ide dec-hi if TF-IDF weighting is used and there is a reasonable number of relevant documents found in the preliminary search. If the preliminary search is so poor that one has to search through many documents to find at least one relevant document, then SVM is preferred.
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paper_title: Update relevant image weights for content-based image retrieval using support vector machines

paper_content:

Relevance feedback (Y. Rui et al., 1998) has been a powerful tool for interactive content based image retrieval (CBIR). During the retrieval process, the user selects the most relevant images and provides a weight of preference for each relevant image. The user's high level query and perception subjectivity can be captured to some extent by dynamically updated low-level feature weights based on the user's feedback. However, in MARS (Y. Rui et al., 1997), only the positive feedbacks, i.e., relevant images are considered. A novel approach is proposed by providing both positive and negative feedbacks for support vector machine (SVM) learning. The SVM learning results are used to update the weights of preference for relevant images. Priorities are given to the positive feedbacks that have larger distances to the hyperplane determined by the support vectors. This approach releases the user from manually providing preference weight for each positive example, i.e., relevant image as before. Experimental results show that the proposed approach has reasonable improvement over relevance feedback with possible examples only.
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paper_title: Gait Analysis for Recognition and Classification

paper_content:

We describe a representation of gait appearance for the purpose of person identification and classification. This gait representation is based on simple features such as moments extracted from orthogonal view video silhouettes of human walking motion. Despite its simplicity, the resulting feature vector contains enough information to perform well on human identification and gender classification tasks. We explore the recognition behaviors of two different methods to aggregate features over time under different recognition tasks. We demonstrate the accuracy of recognition using gait video sequences collected over different days and times and under varying lighting environments. In addition, we show results for gender classification based our gait appearance features using a support-vector machine.
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paper_title: Support Vector Learning for Gender Classification Using Audio and Visual Cues: A Comparison

paper_content:

Computer vision systems for monitoring people and collecting valuable demographics in a social environment will play an increasingly important role in enhancing user's experience and can significantly improve the intelligibility of a human computer interaction (HCI) system. For example, a robust gender classification system is expected to provide a basis for passive surveillance and access to a smart building using demographic information or can provide valuable consumer statistics in a public place. The option of an audio cue in addition to the visual cue promises a robust solution with high accuracy and ease-of-use in human computer interaction systems.This paper investigates the use of Support Vector Machines(SVMs) for the purpose of gender classification. Both visual (thumbnail frontal face) and audio (features from speech data) cues were considered for designing the classifier and the performance obtained by using each cue was compared. The performance of the SVM was compared with that of two simple classifiers namely, the nearest prototype neighbor and the k-nearest neighbor on all feature sets. It was found that the SVM outperformed the other two classifiers on all datasets. The best overall classification rates obtained using the SVM for the visual and speech data were 95.31% and 100%, respectively.
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paper_title: Combining flat and structured representations for fingerprint classification with recursive neural networks and support vector machines

paper_content:

We present new fingerprint classification algorithms based on two machine learning approaches: support vector machines (SVMs) and recursive neural networks (RNNs). RNNs are trained on a structured representation of the fingerprint image. They are also used to extract a set of distributed features of the fingerprint which can be integrated in the SVM. SVMs are combined with a new error-correcting code scheme. This approach has two main advantages: (a) It can tolerate the presence of ambiguous fingerprint images in the training set and (b) it can effectively identify the most difficult fingerprint images in the test set. By rejecting these images the accuracy of the system improves significantly. We report experiments on the fingerprint database NIST-4. Our best classification accuracy is of 95.6 percent at 20 percent rejection rate and is obtained by training SVMs on both FingerCode and RNN-extracted features. This result indicates the benefit of integrating global and structured representations and suggests that SVMs are a promising approach for fingerprint classification.
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paper_title: Face Pose Discrimination Using Support Vector Machines (SVM)

paper_content:

This paper describes an approach for the problem of face pose discrimination using support vector machines (SVM). Face pose discrimination means that one can label the face image as one of several known poses. Face images are drawn from the standard FERET database. The training set consists of 150 images equally distributed among frontal, approximately 33.75/spl deg/ rotated left and right poses, respectively, and the test set consists of 450 images again equally distributed among the three different types of poses. SVM achieved perfect accuracy-100%-discriminating between the three possible face poses on unseen test data, using either polynomials of degree 3 or radial basis functions (RBF) as kernel approximation functions.
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paper_title: Typhoon Analysis and Data Mining with Kernel Methods

paper_content:

The analysis of the typhoon is based on the manual pattern recognition of cloud patterns on meteorological satellite images by human experts, but this process may be unstable and unreliable, and we think could be improved by taking advantage of both the large collection of past observations and the state-of-the-art machine learning methods, among which kernel methods, such as support vector machines (SVM) and kernel PCA, are the focus of the paper. To apply the "learning-from-data" paradigm to typhoon analysis, we built the collection of more than 34,000 well-framed typhoon images to be used for spatio-temporal data mining of typhoon cloud patterns with the aim of discovering hidden and unknown regularities contained in large image databases. In this paper, we deal with the problem of visualizing and classifying typhoon cloud patterns using kernel methods. We compare preliminary results with baseline algorithms, such as principal component analysis and a k-NN classifier, and discuss experimental results with the future direction of research.
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paper_title: Nonlinear perceptual audio filtering using support vector machines

paper_content:

The perceptually based loss functions for audio filtering used by P.J. Wolfe and S.J. Godsill (see Proc. IEEE ICASSP, vol.2, p.821-4, 2000) are shown to fit well within a complex-valued support vector machine (SVM) framework. SVM regression is extended to the estimation of complex-valued functions, including the derivation of a variant of the sequential minimal optimisation (SMO) algorithm. Audio filters are derived using this, based on an autoregressive (AR) model used for audio and two different Hermitian kernel functions. Results are found to be promising, and further improvements are discussed.
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paper_title: Financial time-series prediction using least squares support vector machines within the evidence framework

paper_content:

The Bayesian evidence framework is applied in this paper to least squares support vector machine (LS-SVM) regression in order to infer nonlinear models for predicting a financial time series and the related volatility. On the first level of inference, a statistical framework is related to the LS-SVM formulation which allows one to include the time-varying volatility of the market by an appropriate choice of several hyper-parameters. The hyper-parameters of the model are inferred on the second level of inference. The inferred hyper-parameters, related to the volatility, are used to construct a volatility model within the evidence framework. Model comparison is performed on the third level of inference in order to automatically tune the parameters of the kernel function and to select the relevant inputs. The LS-SVM formulation allows one to derive analytic expressions in the feature space and practical expressions are obtained in the dual space replacing the inner product by the related kernel function using Mercer's theorem. The one step ahead prediction performances obtained on the prediction of the weekly 90-day T-bill rate and the daily DAX30 closing prices show that significant out of sample sign predictions can be made with respect to the Pesaran-Timmerman test statistic.
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paper_title: Cloud classification using support vector machines

paper_content:

Cloud classification from GOES 8 (Geostationary Operational Environmental Satellite) imagery data is performed using the infrared (IR) channel only. For each block of the image, first and second order statistics are extracted and used to train and test a classifier. In this paper, cloud classification is performed using a support vector machine (SVM) classifier. This scheme, which is typically used to solve two-class problems, has been extended to classify ten different cloud and no-cloud areas. Preliminary results indicate the promise of this method for meteorological applications.
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paper_title: Modified support vector machines in financial time series forecasting

paper_content:

This paper proposes a modified version of support vector machines, called C-ascending support vector machine, to model non-stationary financial time series. The C-ascending support vector machines are obtained by a simple modification of the regularized risk function in support vector machines, whereby the recent e-insensitive errors are penalized more heavily than the distant e-insensitive errors. This procedure is based on the prior knowledge that in the non-stationary financial time series the dependency between input variables and output variable gradually changes over the time, specifically, the recent past data could provide more important information than the distant past data. In the experiment, C-ascending support vector machines are tested using three real futures collected from the Chicago Mercantile Market. It is shown that the C-ascending support vector machines with the actually ordered sample data consistently forecast better than the standard support vector machines, with the worst performance when the reversely ordered sample data are used. Furthermore, the C-ascending support vector machines use fewer support vectors than those of the standard support vector machines, resulting in a sparser representation of solution.
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paper_title: Feature extraction and classification of blood cells for an automated differential blood count system

paper_content:

The differential blood counter system we developed is an attempt to automate the task performed manually by experts in routine. Feature extraction and classification are two important components of our automated system. In this paper, classification of blood cells using various approaches including neural network based classifiers and support vector machine are presented together with the features used in the classification.
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paper_title: Goal detection in football by using support vector machines for classification

paper_content:

We present a technique for detecting goals during a football match by using images acquired by a single camera placed externally to the field. The method does not require the modification neither of the ball nor of the goalmouth. Due to the attitude of the camera with respect to the football ground, the system can be thought of as an electronic linesman which helps the referee in establishing the occurrence of a goal during a football match. The occurrence of the event is established by detecting the ball and comparing its position with respect to the location of the goalpost in the image. The ball detection technique relies on a supervised learning scheme called support vector machines for classification. The examples used for training are appropriately filtered version of views of the object to be detected, previously stored in the form of image patterns. We have extensively tested the technique on real images in which the ball is both fully visible and partially occluded. The performance of the proposed detection scheme are measured in terms of detection rate, false positive rate and precision in the ball localisation in image.
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paper_title: Probability prediction using support vector machines

paper_content:

Reports on an investigation of the use of support vector machines in the problem of beating bookmakers' odds. The method used is to create nine regression lines, one for each of the possible half-time/full-time possibilities, e.g. a draw at half time with the home team winning at full time. We extend the support vector machine by incorporating variable-width permissible error bounds based on our knowledge of the sample size on which each of the data points is based. We show that the variable-width method improves the regression.
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paper_title: Bullet-hole image classification with support vector machines

paper_content:

This paper focuses on the application of support vector machines (SVM) for classification of bullet hole images in an auto-scoring system. In order to automatically calculate the score of a shooter, the bullet-hole images can be classified as one, two or more bullet-hole images. For the auto-scoring system, two main issues are considered. One is to extract important features of bullet-hole images from the target paper; the other is to classify these images into correct classes. A set of essential features of bullet-hole images are discussed and used for the subsequent classification. SVM has been applied to the multi-class classification problem. Experimental results show that both the extracted features and SV learning algorithms are effective and efficient for the project.
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paper_title: Selecting bankruptcy predictors using a support vector machine approach

paper_content:

The conventional neural network approach has been found useful in predicting corporate distress from financial statements. We have adapted a support vector machine approach to the problem. A way of selecting bankruptcy predictors is shown, using the Euclidean distance based criterion calculated within the SVM kernel. A comparative study is provided using three classical corporate distress models and an alternative model based on the SVM approach.
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paper_title: Nonstationary signal classification using support vector machines

paper_content:

We demonstrate the use of support vector (SV) techniques for the binary classification of nonstationary sinusoidal signals with quadratic phase. We briefly describe the theory underpinning SV classification, and introduce Cohen's group time-frequency representation, which is used to process the nonstationary signals so as to define the classifier input space. We show that the SV classifier outperforms alternative classification methods on this processed data.
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paper_title: Data condensation in large databases by incremental learning with support vector machines

paper_content:

An algorithm for data condensation using support vector machines (SVM) is presented. The algorithm extracts data points lying close to the class boundaries, which form a much reduced but critical set for classification. The problem of large memory requirements for training SVM in batch mode is circumvented by adopting an active incremental learning algorithm. The learning strategy is motivated from the condensed nearest neighbor classification technique. Experimental results presented show that such active incremental learning enjoy superiority in terms of computation time and condensation ratio, over related methods.
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paper_title: A Tutorial on Support Vector Machines for Pattern Recognition

paper_content:

The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.
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paper_title: Improving Support Vector Machine Classifiers by Modifying Kernel Functions

paper_content:

We propose a method of modifying a kernel function to improve the performance of a support vector machine classifier. This is based on the structure of the Riemannian geometry induced by the kernel function. The idea is to enlarge the spatial resolution around the separating boundary surface, by a conformal mapping, such that the separability between classes is increased. Examples are given specifically for modifying Gaussian Radial Basis Function kernels. Simulation results for both artificial and real data show remarkable improvement of generalization errors, supporting our idea.
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paper_title: Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines

paper_content:

This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possible QP problems. These small QP problems are solved analytically, which avoids using a time-consuming numerical QP optimization as an inner loop. The amount of memory required for SMO is linear in the training set size, which allows SMO to handle very large training sets. Because matrix computation is avoided, SMO scales somewhere between linear and quadratic in the training set size for various test problems, while the standard chunking SVM algorithm scales somewhere between linear and cubic in the training set size. SMO’s computation time is dominated by SVM evaluation, hence SMO is fastest for linear SVMs and sparse data sets. On realworld sparse data sets, SMO can be more than 1000 times faster than the chunking algorithm.
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paper_title: Improvements to Platt's SMO Algorithm for SVM Classifier Design

paper_content:

This article points out an important source of inefficiency in Platt's sequential minimal optimization (SMO) algorithm that is caused by the use of a single threshold value. Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO. These modified algorithms perform significantly faster than the original SMO on all benchmark data sets tried.
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Title: A SURVEY ON PATTERN RECOGNITION APPLICATIONS OF SUPPORT VECTOR MACHINES
Section 1: Introduction
Description 1: Provide a brief overview of Support Vector Machines (SVMs) and their significance in pattern recognition applications.

Section 2: Support Vector Machines
Description 2: Explain the fundamental concepts and workings of SVMs, including their generalization abilities and principles (ERM and SRM).

Section 3: Linear support vector machines for linearly separable case
Description 3: Describe the approach for constructing hyperplanes in linearly separable cases and the optimization techniques involved.

Section 4: Linear support vector machines for nonseparable case
Description 4: Discuss the modifications required for handling non-linearly separable data, including the introduction of slack variables and the tradeoff parameter.

Section 5: Inner-product kernels
Description 5: Explain the concept of kernel functions and their role in transforming data for nonlinear decision boundaries.

Section 6: Dual problem
Description 6: Describe the formulation of the dual problem in SVM optimization and techniques for solving large-scale quadratic programming challenges.

Section 7: SVMs applied to multiclass classification
Description 7: Outline the strategies for adapting SVMs to multiclass problems, including one-to-others and tree-structured approaches.

Section 8: Pattern Recognition Applications of SVMs
Description 8: Survey the various applications of SVMs in pattern recognition, categorized by their specific goals and methodologies.

Section 9: Face detection and recognition
Description 9: Detail the use of SVMs in face-related applications, highlighting different approaches and performance comparisons.

Section 10: Object detection and recognition
Description 10: Review the application of SVMs in object detection and recognition, including techniques for handling different poses and views.

Section 11: Handwritten character recognition
Description 11: Discuss the application of SVMs in handwriting recognition, emphasizing feature extraction and classifier performance.

Section 12: Speaker recognition and speech recognition
Description 12: Explain the usage of SVMs in speaker and speech recognition, comparing discriminative classifiers with generative model approaches.

Section 13: Information and image retrieval
Description 13: Describe the application of SVMs in content-based image retrieval and other information retrieval tasks.

Section 14: Other applications
Description 14: Survey additional applications of SVMs in various fields such as gender classification, intrusion detection, texture classification, and more.

Section 15: Performance Issues
Description 15: Discuss the factors affecting SVM performance, including kernel selection, training speed, and handling large datasets.

Section 16: Conclusions
Description 16: Summarize the key findings of the survey, the advantages of using SVMs, and potential future research directions.