Article

The Entire Regularization Path for the Support Vector

04/2004;
Source: CiteSeer

ABSTRACT The Support Vector Machine is a widely used tool for classification. Many e#cient implementations exist for fitting a two-class SVM model. The user has to supply values for the tuning parameters: the regularization cost parameter, and the kernel parameters. It seems a common practice is to use a default value for the cost parameter, often leading to the least restrictive model. In this paper we argue that the choice of the cost parameter can be critical. We then derive an algorithm that can fit the entire path of SVM solutions for every value of the cost parameter, with essentially the same computational cost as fitting one SVM model. We illustrate our algorithm on some examples, and use our representation to give further insight into the range of SVM solutions.

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Keywords

algorithm
 
common practice
 
computational cost
 
cost parameter
 
default value
 
e#cient implementations
 
kernel parameters
 
regularization cost parameter
 
restrictive model
 
supply values
 
Support Vector Machine
 
SVM model
 
SVM solutions
 
tuning parameters
 
two-class SVM model
 
used tool