Improving SVM Classification by Feature Weight Learning

摘要：

This paper presents a new feature weighting method to improve the performance of support vector machine (SVM). The basic idea of this method is to translate the feature weight learning into the problem of choosing a kernel suitable for SVM classification. In more detail, this method tunes the width parameters of Gaussian ARID (Automatic Relevance Determination) kernel via optimizing a kernel evaluation criterion, i.e., kernel polarization. By using gradient ascent technique, each learned parameter indicates the relative importance of the corresponding feature. The proposed method is demonstrated with some UCI machine learning benchmark examples.

关键词： feature weighting support vector machine (SVM) kernel polarization Gaussian kernel auto relevance determination (ARD)

作者: Tinghua Wang

作者单位: School of Mathematics and Computer Science Gannan Normal University Ganzhou 341000, PR China

会议类型: 国际会议

会议名称: 2010 International Conference on Intelligent Computation Technology and Automation(2010 智能计算技术与自动化国际会议 ICICTA 2010)

会议地点: 长沙

会议语种:英文

页码: 1688-1691

在线出版日期: 2010-05-11（万方平台首次上网日期，不代表论文的发表时间）