The learning performance of support vector machine classification based on Markov sampling  

作　　者：ZOU Bin PENG ZhiMing XU ZongBen 

机构地区：[1]Faculty of Mathematics and Computer Science,Hubei University [2]Institute for Information and System Science,Xi'an Jiaotong University

出　　处：《Science China(Information Sciences)》2013年第3期103-118,共16页中国科学（信息科学）（英文版）

基　　金：supported by National Basic Research Program of China(Grant No.2007CB311002);State Key Program of National Natural Science of China(Grant No.70501030);National Natural Science Foundation of China(Grant No.61070225);China Postdoctoral Science Foundation(Grant Nos.20080440190,200902592);Foundation of Hubei Educational Committee(Grant No.Q20091003)

摘　　要：The previously known frameworks describing the consistency of support vector machine classifica- tion （SVMC） algorithm are usually based on the assumption of independent and identically distributed （i.i.d.） samples. In this paper we go far beyond these classical frameworks by studying the consistency of SVMC algo- rithm with uniformly ergodic Markov chain samples based on linear prediction models. We establish the bound on the consistency of SVMC algorithm with uniformly ergodic Markov chain samples, and show that SVMC algorithm with uniformly ergodic Markov chain samples is consistent. Inspired by the idea from Markov chain Monto Carlo （MCMC） methods, we introduce a new Markov sampling algorithm for classification to generate uniformly ergodic Markov chain samples from large data set, and present numerical studies on simulated data and benchmark repository using SVMC algorithm.The previously known frameworks describing the consistency of support vector machine classifica- tion （SVMC） algorithm are usually based on the assumption of independent and identically distributed （i.i.d.） samples. In this paper we go far beyond these classical frameworks by studying the consistency of SVMC algo- rithm with uniformly ergodic Markov chain samples based on linear prediction models. We establish the bound on the consistency of SVMC algorithm with uniformly ergodic Markov chain samples, and show that SVMC algorithm with uniformly ergodic Markov chain samples is consistent. Inspired by the idea from Markov chain Monto Carlo （MCMC） methods, we introduce a new Markov sampling algorithm for classification to generate uniformly ergodic Markov chain samples from large data set, and present numerical studies on simulated data and benchmark repository using SVMC algorithm.

关 键 词：SVMC uniformly ergodic Markov chain learning performance Markov sampling linear predictionmodels 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]