Support Vector Machines
Training and

Applications-The Support Vector Machine
SVM is a new and very promising classication technique developed by Vapnik and

his group at ATT Bell Laboratories   　　



This new learning algorithm can be seen as an alternative training

technique for Polynomial Radial Basis Function and MultiLayer Perceptron classiers

The main idea behind the

technique is to separate the classes with a surface that maximizes the margin between them

An interesting property

of this approach is that it is an approximate implementation of the Structural Risk Minimization
SRM induction

principle 



The derivation of Support Vector Machines its relationship with SRM and its geometrical insight

are discussed in this paper



Since Structural Risk Minimization is an inductive principle that aims at minimizing a bound on the generalization

error of a model rather than minimizing the Mean Square Error over the data set
as Empirical Risk Minimization

methods do training a SVM to obtain the ma