现代电子技术2011，Vol.34Issue(13)：165-167,171,4.
支撑向量机多类分类方法的研究
Research on Multi-class Classification of Support Vector Machine
胡振新 1李宏 1郭泽华1
作者信息
- 1. 西北工业大学电子信息学院，陕西西安 710072
- 折叠
摘要
Abstract
Support vector machine(SVM) is a new learning method based on statistical learning theory, which can effectively solve the over study problem by using structural risk minimization (SRM) and has better generalization performance. Traditional SVM is developed for binary classification problems, in order to analyze huge and multi-category data for practical problems, a comparison result about the classification speed and accuracy is given through analyzing the theory and realization method of all-together, one-against-rest, one-against-one and directed acyclic graph sup- port vector machine(DAGSVM). Experimental results show that various methods can get different classifier generalization ability, training speed and test speed. The direction of how to solve multi-class classification effectively is proposed.关键词
统计学理论；支撑向量机；结构风险最小化；多类分类Key words
statistical learning theory/ support vector machine (SVM)/ structural risk minimization (SRM)/ multi-class classification分类
信息技术与安全科学引用本文复制引用
胡振新,李宏,郭泽华..支撑向量机多类分类方法的研究[J].现代电子技术,2011,34(13):165-167,171,4.
