THE RESEARCH OF THE FAST SVM CLASSIFIER METHOD

被引：0

作者：

Yang, Yujun ^{[1
,2
,3
]}

Li, Jianping ^{[1
]}

Yang, Yimei ^{[2
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China

[2] Huaihua Univ, Dept Comp Sci & Technol, Huaihua 418008, Peoples R China

[3] Hunan Prov Key Lab Ecol Agr Intelligent Control T, Huaihua 418008, Peoples R China

来源：

2015 12TH INTERNATIONAL COMPUTER CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (ICCWAMTIP) | 2015年

基金：

国家高技术研究发展计划(863计划); 中国国家自然科学基金;

关键词：

SVM; Classifier; Framework; Support vector machine;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Support vector machine (SVM) is a machine learning method developed in the mid-1990s based on statistical learning theory. SVM classifier is currently more popular classifier. This paper presents a boundary detection technique for retaining the potential support vector. Through seeking to structural risk minimization of the SVM, it improves the learning generalization ability and achieves the minimization of empirical risk and confidence range in the case of small statistical sampIe size and it can also obtain the desired good statisticallaw.

引用

页码：121 / 124

页数：4

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