title: The generalization ability of SVM classification based on Markov sampling.
abstract: The previously known works studying the generalization ability of support vector machine classification (SVMC) algorithm are usually based on the assumption of independent and identically distributed samples. In this paper, we go far beyond this classical framework by studying the generalization ability of SVMC based on uniformly ergodic Markov chain (u.e.M.c.) samples. We analyze the excess misclassification error of SVMC based on u.e.M.c. samples, and obtain the optimal learning rate of SVMC for u.e.M.c.
  
  
  METHODS
  We also introduce a new Markov sampling algorithm for SVMC to generate u.e.M.c. samples from given dataset, and present the numerical studies on the learning performance of SVMC based on Markov sampling for benchmark datasets. The numerical studies show that the SVMC based on Markov sampling not only has better generalization ability as the number of training samples are bigger, but also the classifiers based on Markov sampling are sparsity when the size of dataset is bigger with regard to the input dimension.
authors.forename: Jie
authors.initials: J
authors.lastname: Xu
authors.forename: Yuan Yan
authors.initials: YY
authors.lastname: Tang
authors.forename: Bin
authors.initials: B
authors.lastname: Zou
authors.forename: Zongben
authors.initials: Z
authors.lastname: Xu
authors.forename: Luoqing
authors.initials: L
authors.lastname: Li
authors.forename: Yang
authors.initials: Y
authors.lastname: Lu
authors.forename: Baochang
authors.initials: B
authors.lastname: Zhang