文件名称:sv-memo
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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
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
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