Support vector machines for multi-class signal classification with unbalanced samples

被引：0

作者：

Xu, P ^{[1
]}

Chan, AK ^{[1
]}

机构：

[1] Texas A&M Univ, Dept Elect Engn, College Stn, TX 77840 USA

来源：

PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2003, VOLS 1-4 | 2003年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Support vector machines (SVMs) were originally developed for binary classification. To extend it to multi-class pattern recognition, one popular approach is to consider the problem as a collection of binary classification problems, so that each of them may be solved by a binary SVM. However, there is no guarantee that these SVMs will achieve the optimal solution even though each individual binary SVM is well trained. In this paper, we propose a method to optimize the multi-class SVMS by adjusting the penalty parameters using a Genetic Algorithm (GA), The method is applied to an acoustic signal classification problem with very promising results.

引用

页码：1116 / 1119

页数：4

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