A Fast Learning Algorithm for One-Class Support Vector Machine

Abstract

Support vector machine (SVM) is a powerful tool to solve classification problems, this paper proposes a fast sequential minimal optimization (SMO) algorithm for training one-class support vector regression (OCSVM), firstly gives a analytical solution to the size two quadratic programming (QP) problem, then proposes a new heuristic method to select the working set which leads to algorithm's faster convergence. The simulation results indicate that the proposed SMO algorithm can reduce the training time of OCSVM, and the performance of proposed SMO algorithm is better than that of original SMO algorithm.