[1]吴 青,刘三阳,郑 巍.基于乘性规则的支持向量机[J].智能系统学报,2007,2(2):74-77.
WU Qing,LIU San-yang,ZHENG Wei.Support vector machines based on multiplicative updates[J].CAAI Transactions on Intelligent Systems,2007,2(2):74-77.
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WU Qing,LIU San-yang,ZHENG Wei.Support vector machines based on multiplicative updates[J].CAAI Transactions on Intelligent Systems,2007,2(2):74-77.
基于乘性规则的支持向量机
《智能系统学报》[ISSN1673-4785/CN23-1538/TP] 卷:
2
期数:
2007年2期
页码:
74-77
栏目:
学术论文—智能系统
出版日期:
2007-04-25
- Title:
- Support vector machines based on multiplicative updates
- 文章编号:
- 1673-4785(2007)02-0074-04
- Keywords:
- support vector machine; quadratic convex programming; mixed constraint ; multiplicative update
- 分类号:
- TP18
- 文献标志码:
- A
- 摘要:
- 传统的二次规划由于涉及大量的矩阵运算，运算速度慢成为支持向量机的最大缺点. 已有的乘性规则仅适于非负二次凸规划问题，推导出了求解支持向量机中混合约束二次凸规划的乘性规则，利用这一乘性规则极大地提高了优化速度．该方法提供了一种直接优化的方法，其所有变量可以并行迭代，乘性规则可以使得二次规划的目标函数单调下降到它的全局最小点.仿真试验结果表明了该算法有效性.
- Abstract:
- Due to intensive matrix computation, the speed of the quadratic equati on remains slow. The multiplicative updates available are only suited for nonneg ative quadratic convex programming. In this article, the multiplicative updates are derived for mixed constraint optimizations, dramatically speeding up optimiz ation rate. This method provides an extremely straightforward way to implement s upport vector machines（SVMs） where all the variables can be iterated in parallel. The multiplicative updates converge to global minimum point by monoton ically reducing the target function of quadratic programming. Experimental resul ts have confirmed the effectiveness of our approach.
- 参考文献/References:
-
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【3］SHA F, SAUL L K，LEE D D. Multiplicative updates for nonnegative qu ad ratic programming in support vector machines ［A］. Advances in Neural and Infor mation Processing Systems［C］.Cambridge, MA，2003.
［4］陆从德,张太镒,胡金燕．基于乘性规则的支持向量域分类器 ［J］．计算机学报, 2004, 27(5): 690-694.
?LU Congde,ZHANG Taiyi,HU Jinyan. Support vector domain classifier based on mu ltiplicative updates ［J］. Chinese Journal of Computers, 2004, 27(5): 691-694.
［5］SHA F,SAUL L K,LEE D. Multiplicative updates for large margin classi fiers［A］. Lecture notes in computer science［C］.Washington,2003.
［6］PLATT J C. Sequential minimal optimization: a fast algorithm for tr aining support vector machines ［Z］. In Scholkopf B, Burges C, Smola A, edito rs, Advances in Kernel Methods: Support Vector Machines. Cambridge, MA: MIT Pres s, 1998.
［7］CRISTIANINI N, CAMPBELL C, SHAWETAYLOR J. Multiplicative updatings for support vector machines［A］. In proceedings of ESANN［C］.Brussels, 1999 .
［8］SHA F, SAUL L K, LEE D D. Multiplicative updates for nonnegative qu adratic programming in support vector machines［A］. In S.Becker, S.Thrun, and K . Obermayer, editors, Advances in Neural and Information Processing Systems［C］ . Cambridge, MA,2003.
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备注/Memo
收稿日期：2006-09-30.
基金项目：国家自然科学基金资助项目（60574075）
作者简介：
吴 青，女，1975年生，博士研究生，主要研究方向为模式识别、支持向量机、最优化理论及其应用.E-mail: qwu@mail.xidian.edu.cn.
刘三阳，男，1959年生，博士生导师，主要研究方向为最优化理论方法、数据挖掘、支持向量机.承担并完成多个国家自然科学基金项目. 在国内外重要期刊上发表论文200多篇，被SCI、EI检索70余篇.E-mail: liusanyang@126.c om.
郑 巍，男，1982年生，博士研究生，主要研究方向为人工智能、数据挖掘. E-mail: open2123@126.com
更新日期/Last Update:
2009-05-06