Using class-center vectors to build support vector machines

A support vector machine builds the final classification function on only a small part of the training samples (the support vectors). It is believed that all the information about classification in the training set can be represented by these samples. However, this is actually not always true when the training set is polluted by noises (training data are not i.i.d.). We present a different method for the problem, which applies the idea of capacity control in SVM but tries to make the machine less sensitive to noises and outliers. The new method can be called a central support vector machine or CSVM, for it uses the class centers in building the support vector machine