Statistics for Machine Learning

Support vector classifier

Support vector classifiers are an extended version of maximum margin classifiers, in which some violations are tolerated for non-separable cases in order to create the best fit, even with slight errors within the threshold limit. In fact, in real-life scenarios, we hardly find any data with purely separable classes; most classes have a few or more observations in overlapping classes.

The mathematical representation of the support vector classifier is as follows, a slight correction to the constraints to accommodate error terms:

In constraint 4, the C value is a non-negative tuning parameter to either accommodate ...