The Generalization Ability of Online SVM Classification Based on Markov Sampling

• Published in: IEEE transaction on neural networks and learning systems 2015-03, Vol.26 (3), p.628-639
• Main Authors: Xu, Jie, Yan Tang, Yuan, Zou, Bin, Xu, Zongben, Li, Luoqing, Lu, Yang
• Format: Article
• Language: English
• Subjects: Algorithm design and analysis; Algorithms; Classification; Classification algorithms; Educational institutions; Generalization ability; Learning; Machine learning algorithms; Markov processes; Markov sampling; Neural networks; Online; online support vector machine (SVM) classification; Sampling; Support vector machines; Training; uniformly ergodic Markov chain (u.e.M.c.)
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2014.2361026

In this paper, we consider online support vector machine (SVM) classification learning algorithms with uniformly ergodic Markov chain (u.e.M.c.) samples. We establish the bound on the misclassification error of an online SVM classification algorithm with u.e.M.c. samples based on reproducing kernel Hilbert spaces and obtain a satisfactory convergence rate. We also introduce a novel online SVM classification algorithm based on Markov sampling, and present the numerical studies on the learning ability of online SVM classification based on Markov sampling for benchmark repository. The numerical studies show that the learning performance of the online SVM classification algorithm based on Markov sampling is better than that of classical online SVM classification based on random sampling as the size of training samples is larger.