Design Methodology of Queue-Based Fast Classification for Sequential Minimal Optimization in SVM ML-Training

Abstract

In this paper, we propose a design methodology of queue-based fast classification for sequential minimal optimization (SMO) in support vector machine (SVM) training. The queue is designed to tremendously reduce the searching space of weightings. Our method is useful to simplify operating steps of SMO and almost achieve the same performance in terms of classification accuracy with respect to full-search approach. In the Matlab simulation, our method is completely verified with 6 representative data sets. As compared to full-search and heuristic approaches, the running speed of our method is increased by 7.53 and 2.91 times, respectively. It features high efficiency without sacrificing classification accuracy.