Support Vector Machines (SVMs)

Abstract

This Chapter details a class of learning mechanisms known as the Support Vector Machines (SVMs). We start by giving the machine learning framework, define and introduce the concepts of linear classifiers, and describe formally the SVMs as large margin classifiers. We focus on the convex optimization problem and in particular we deal with the Sequential Minimal Optimization (SMO) which is crucial to proceed to implement the algorithm. Finally we detail issues of the SVMs implementation. Regarding the latter, several aspects related to CPU and GPU implementation are surveyed. Our aim is two fold: first, we implement the multi-thread CPU version, test it in benchmark data sets; then we proceed with the GPU version. We intend to give a clear understanding of specific aspects related to the implementation of basic SVM machines in a many-core perspective. Further developments can easily be extended to other SVM variants launching one step further the potential for big data adaptive machines.