基于熵理论和核密度估计的最大间隔学习机  

A Maximum Margin Learning Machine Based on Entropy Concept and Kernel Density Estimation

作　　者：刘忠宝[1,2] 王士同[1] 

机构地区：[1]江南大学数字媒体学院,无锡214122 [2]山西大学商务学院信息学院,太原030031

出　　处：《电子与信息学报》2011年第9期2187-2191,共5页Journal of Electronics & Information Technology

基　　金：国家863计划项目(2007AA1Z158);国家自然科学基金(60773206;60704047)资助课题

摘　　要：该文针对支持向量机(SVM)及其变种的不足,提出一种基于熵理论和核密度估计的最大间隔学习机MLMEK。MLMEK引入了核密度估计和熵的概念,用核密度估计表征样本数据的分布特征,用熵表征分类的不确定性。MLMEK真实反映样本数据的分布特征;同时解决两类分类问题和单类分类问题;比传统SVM具有更好的分类性能。UCI数据集上的实验验证了MLMEK的有效性。In order to circumvent the deficiencies of Support Vector Machine（SVM） and its improved algorithms,this paper presents Maximum-margin Learning Machine based on Entropy concept and Kernel density estimation（MLMEK）.In MLMEK,data distributions in samples are represented by kernel density estimation and classification uncertainties are represented by entropy.MLMEK takes boundary data between classes and inner data in each class seriously,so it performs better than traditional SVM.MLMEK can work for two-class and one-class pattern classification.Experimental results obtained from UCI data sets verify that the algorithms proposed in the paper is effective and competitive.

关 键 词：模式识别 熵理论 核密度估计 支持向量机 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]