Analysis of detectors for support vector machines and least square support vector machines
Anthony Kuh · 2003
This paper discusses the performance capabilities of the support vector machine (SVM) and the least squares SVM (LS-SVM) for a two hypothesis detection problem. We consider a Bayesian framework where there are priors associated with each hypothesis and costs for making decisions. We examine how the SVM and the LS-SVM compare with the optimal Bayesian solution. We also discuss other merits for the SVM and the LS-SVM including practical implementation.