山西大学学报（自然科学版）2012，Vol.35Issue(2)：224-230,7.
基于数据关系的SVM多分类学习算法
A Multi-Classification SVM Algorithm Based on Data Relationship
摘要
Abstract
A multi-class support vector machine(SVM) algorithm was introduced based on data relationship (DR-SVM). Through extracting redundant information of subclassifiers based on relationship between different class data (such as inner product of vectors and so on),the DR-SVM model can reduce the number of subclassifiers. Then the multi-classifiers can be trained by traditional SVM. In so doing, the obtained model can be simplified and the satisfactory generalization performance can be reached at same time. The experiment results on benchmark datasets demonstrate that comparing with several traditional multi-class SVM approaches,the DR-SVM possesses better performance. Especially,it is more efficient for some data processing problems like the predicting precision of individual class should be not under a threshold.关键词
支持向量机/多分类/数据关系/泛化能力Key words
support vector machine/multi-classification/data relationship/generalization performance分类
信息技术与安全科学引用本文复制引用
王文剑,梁志,郭虎升..基于数据关系的SVM多分类学习算法[J].山西大学学报（自然科学版）,2012,35(2):224-230,7.基金项目
国家自然科学基金(60975035) （60975035）
教育部博士点基金(20091401110003) （20091401110003）
山西省自然科学基金(2009011017-2) （2009011017-2）
山西省研究生创新项目(20103021) （20103021）
