How unlabeled data help in semi-supervised learning?
Does unlabeled data really help in semi-supervised learning?
What are some supervised and unsupervised machine learning methods for working with data-set that have very large features and low number of samples? What are the best methods and algorithms for unsupervised learning in R? Why are unsupervised methods or reconstruction methods such as autoencoders, better than supervised methods such as logistic regression for pre-training (greedy) deep learning layers? What's the most efficient classification algorithm for unbalanced data sets? And what pre-processing could be done to optimize the score? What are some of deep learning models that can learn to differentiate between reliable and noisy training data? Did deep learning revolutionize clustering as it did for supervised learning? What are some good methods for data pre-processing in machine learning? Where can machine learning not be applied? How can I use Deep Learning in selecting features, and not training/predicting the data? How data analysis is different from machine learning? Can Dropout be used to avoid overfitting a deep learning model with very small training data? What applications of machine learning are underexplored? How do you explain Machine Learning and Data Mining to non Computer Science people? Is deep learning overhyped? How do you handle inconsistent time series data for deep learning? What is a training data set & test data set in machine learning? What are the rules for selecting them? Is Unsupervised pre-training and Greedy layer-wise pre-training methods considered obsolete for modern Deep Learning? What is top-down learning? What is open-source ensemble/committee of convolutional neural networks implementation? Is any type of cross validation not used in Deep Learning? What do useful classification systems not do? Is datascience easy to learn? Deep Learning: Does a smooth version of rectified linear units work better or worse than a non-smooth one? What is inductive learning? For what use cases do you think deep learning and neural networks overkill and simpler models work better in practice? How can I better enable non data scientists to create insight from their data? Data discovery and munging is still an issue. How does learning happen? How does Forkable use machine learning? In classification, how do you handle an unbalanced training set? How do the fields of Data Mining, Machine Learning and Big Data relate to each other?