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

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.