Extensions of the SVM Method to the Non-Linearly Separable Data

The main aim of the paper is to briefly investigate the most significant topics of the currently used methodologies of solving and implementing Support Vector Machine (SVM)-based classifiers. Following a brief introductory part, the basics of linear SVM and non-linear SVM models are briefly exposed in the next two sections. The problem of soft margin SVMs is exposed in the fourth section of the paper. Several approaches have been proposed aiming to reduce the computation complexity, like the interior point method, decomposition methods such as Sequential Minimal Optimization, and the gradient-based method, to solve the primal SVM problem. Several approaches based on genetic search in solving the more general problem of identifying the optimal type of kernel from a pre-specified set of kernel types have been recently proposed. The fifth section of the paper is a brief survey on the most outstanding new techniques reported so far in this respect.