Paper Info
Title
Choosing the Parameters of 2-norm Soft Margin Support Vector Machines According to the Cluster Validity
Abstract
Determining the kernel and error penalty parameters for support vector machines (SVMs) is very problem-dependent in practice. This paper proposes using a cluster validation index in the feature space to help choose parameters for training 2-norm soft margin support vector machines. With the proposed method, the kernel parameters and the penalty parameter of the error term in the 2-norm soft margin SVM are considered to be the parameters of an alternative kernel for a hard margin SVM. Thus the values of cluster validation index can be calculated in the feature spaces which are defined by the kernels with the parameters. The cluster validation index shows whether the data are well-separated in a feature space, so it can be used to determine whether a combination of the kernel parameters leads to a feature space in which the data are easy to be classified. It guides the search of parameters toward a good testing accuracy, so the search range of the parameters is confined to a small region, and the parameters selecting time of the SVM training process can be shortened.
Year
DOI
Venue
2006
10.1109/ICSMC.2006.385069
SMC
Keywords
Field
DocType
pattern clustering,cluster validity,two-norm soft margin support vector machines,cluster validation index,parameter choosing,error penalty parameters,kernel parameters,support vector machines,support vector machine,indexation,feature space
Graph kernel,Feature vector,Radial basis function kernel,Pattern recognition,Least squares support vector machine,Computer science,Support vector machine,Polynomial kernel,Artificial intelligence,Kernel method,Margin classifier,Machine learning
Conference
Volume
ISSN
ISBN
6
1062-922X
1-4244-0100-3
Citations 
PageRank 
References 
0
0.34
8
Authors
2
Name
Order
Citations
PageRank
Kuo-Ping Wu123712.06
Sheng-De Wang272068.13