ISSN 1000-1239 CN 11-1777/TP

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支持向量机最优模型选择的研究

刘向东 骆 斌 陈兆乾   

  1. (南京大学计算机软件新技术国家重点实验室 南京 210093) (南京大学计算机科学与技 术系 南京 210093) (Liuxd999@Sohu.com)
  • 出版日期: 2005-04-15

Optimal Model Selection for Support Vector Machines

Liu Xiangdong, Luo Bin, and Chen Zhaoqian   

  1. (State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093) (Department of Computer Science & Technology, Nanjing University, Nanj ing 210093)
  • Online: 2005-04-15

摘要: 通过对核矩阵的研究,利用核矩阵的对称正定性,采用核校准的方法提出了一种SVM最优模 型选择的算法--OMSA算法.利用训练样本不通过SVM标准训练和测试过程而寻求最优的核参 数和相应的最优学习模型,弥补了传统SVM在模型选择上经验性强和计算量大的不足.采用该 算法在UCI标准数据集和FERET标准人脸库上进行了实验,结果表明,通过该算法找到的核参 数以及相应的核矩阵是最优的,得到的SVM分类器的错误率最小.该算法为SVM最优模型选择 提供了一种可行的方法,同时对其他基于核的学习方法也具有一定的参考价值.

关键词: 支持向量机, 核参数, 核校准, 模型选择

Abstract: Proposed in this paper is a method of model selection based on kernel alignment for support vector machines-OMSA (optimal model selection algorithm) by means o f learning on kernel matrix. This algorithm aims at finding the optimal kernel p arameters and learning model from training data without performing the standard procedures of SVM training and testing so as to overcome the flaws of convention al methods of SVM model selection. The classification experiments on the UCI dat abase and the face recognition experiments on the FERET face database are deploy ed with this algorithm and the famous LOO (leave-one-out) algorithm. The four da tasets from UCI used in experiments are diabetis, glass, waveform and wine. By comparison with the LOO algorithm, the experimental results show that the optimal kernel parameters and kernel matrix are found by OMSA with the minimal testing error of SVM classifier. Specially, the results from face recognition experiment s are satisfactory. This algorithm provides a feasible method for SVM model sele ction as well as references for other kernel-based learning algorithms.

Key words: support vector machine, kernel parameter, kernel alignment, model selection