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支持向量机训练算法综述
引用本文:刘江华,程君实,陈佳品.支持向量机训练算法综述[J].信息与控制,2002,31(1):45-50.
作者姓名:刘江华  程君实  陈佳品
作者单位:上海交通大学信息存储研究中心,上海,200030
摘    要:本文介绍统计学习理论中最年轻的分支——支持向量机的训练算法,主要有三大类:以 SVMlight为代表的分解算法、序贯分类方法和在线训练法,比较了各自的优缺点,并介绍了 其它几种算法及多类分类算法.最后指出了支持向量机具体实现的方向及其在模式识别、数 据挖掘、系统辨识与控制等领域中的应用.

关 键 词:支持向量机  训练算法  统计学习理论
文章编号:1002-0411(2002)01-045-06

SUPPORT VECTOR MACHINE TRAINING ALGORITHM: A REVIEW
LIU Jiang,hua,CHENG Jun,shi,CHEN Jia,pin.SUPPORT VECTOR MACHINE TRAINING ALGORITHM: A REVIEW[J].Information and Control,2002,31(1):45-50.
Authors:LIU Jiang  hua  CHENG Jun  shi  CHEN Jia  pin
Abstract:This article introduced the training algorithm for the newest branch of statistic learning theory, SVM(Support Vector Machine), which can be classified into three categories: the first is the Decomposition Algorithm, whose delegate is SVMlight , the second is sequence algorithm, the third is online training algorithm. All the three kinds of algorithms' advantages and disadvantages were analysed. And other algorithms and multi class algorithms are introduced too. The future direction and application of SVM in pattern recognition and data mining, and so on were introduced.
Keywords:support vector machine  training algorithm  statistical learning theory
本文献已被 CNKI 维普 万方数据 等数据库收录!
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