 An object's characteristics are also known as feature values and are typically presented to the machine in a vector called a feature vector.Such classifiers work well for practical problems such as document classification, and more generally for problems with many variables (features), reaching accuracy levels comparable to non-linear classifiers while taking less time to train and use.If the input feature vector to the classifier is a real vector formula_1, then the output score is

where formula_3 is a real vector of weights and "f" is a function that converts the dot product of the two vectors into the desired output.