In this chapter the idea of using Support Vector Machines in pattern classification is presented. The formulation of SVM is constructed starting from a simple linear maximum margin classifier. The importance of capacity control to avoid over fitting discussed. Finally the claim that SVM training achieves the lowest necessary capacity for a given classification task will be investigated.
A general two-class pattern classification problem is posed as follows :
The performance of the classifier is measured in terms of classification error which is defined in Eqn. .