Improving SVM through a Risk Decision Rule Running on MATLAB
Xuemei Zhang, Yang Li · Journal of Software · 2012
Support Vector Machine (SVM) is a classification technique based on Structural Risk Minimization (SRM), which can run on MATLAB. For classification of nonseparable samples, conventional SVM needs to select a tradeoff between maximization the margin and misclassification rate. In order to guarantee generalized performance and low misclassification rate of SVM, this paper puts forward an improved SVM through a risk decision rule for the nonseparable samples running on MATLAB. The improved SVM transforms the outputs of the SVM to posterior probabilities belonging to different classes and samples between the support hyper-planes are classified by using risk decision rule of Empirical Risk Minimization (ERM). Computational results show that the proposed approach is better than conventional SVM remarkably when the two classes are easy to separate, and in other condition, its performance is comparable to conventional SVM.