[1]王书舟,伞 冶.支持向量机的训练算法综述[J].智能系统学报,2008,(06):467-475.
 WANG Shu-zhou,SAN Ye.A survey on training algorithms for support vector machine[J].CAAI Transactions on Intelligent Systems,2008,(06):467-475.
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支持向量机的训练算法综述(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
期数:
2008年06期
页码:
467-475
栏目:
出版日期:
2008-12-25

文章信息/Info

Title:
A survey on training algorithms for support vector machine
文章编号:
1673-4785(2008)06-0467-09
作者:
王书舟 伞 冶
哈尔滨工业大学 控制与仿真中心,黑龙江 哈尔滨 150001
Author(s):
WANG Shu-zhouSAN Ye
Control & Simulation Centre, Harbin Institute of Technology, Harbin 150001, China
关键词:
统计学习理论支持向量机训练算法
Keywords:
statistical learning theorysupport vector machinetraining algorithms
分类号:
TP391.9
文献标志码:
A
摘要:
支持向量机(SVM)是在统计学习理论基础上发展起来的新方法,其训练算法本质上是一个二次规划的求解问题.首先简要概述了SVM的基本原理,然后对SVM训练算法的国内外研究现状进行综述,重点分析SVM的缩减算法和具有线性收敛性质的算法,对这些算法的性能进行比较,并且对SVM的扩展算法也进行简单介绍.最后对该领域存在的问题和发展趋势进行了展望.
Abstract:
Support vector machines (SVMs) use new methods that originated in statistical learning theory. Training of an SVM can be formulated as a quadratic programming problem. The principles of SVM have been summarized briefly in this paper. The latest developments in SVM training algorithms in domestic and overseas research were reviewed, especially reduction algorithms and algorithms with linear convergence properties. The performance of these algorithms was then compared, and a brief introduction to a proposed extension of them was given. Finally some problems and potential directions for future research are discussed.

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备注/Memo

备注/Memo:
收稿日期:2008-06-30.
基金项目:国家自然科学基金资助项目(60474069).
作者简介:
王书舟,男,1972年生,博士研究生,主要研究方向为支持向量机建模、直升机控制与仿真.发表学术论文多篇,6篇被EI检索.
伞    冶,男,1951年生,教授,博士生导师.中国系统仿真学会理事,黑龙江省优秀中青年专家,享受国务院政府特殊津贴.主要研究方向为复杂大系统的系统控制与仿真.获国家科技进步二等奖2项,三等奖1项,省部级科技进步奖多项.发表学术论文多篇,40余篇被EI收录.
更新日期/Last Update: 2009-04-03