Paper Info
Title
The generalization ability of online SVM classification based on Markov sampling.
Abstract
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.
Year
DOI
Venue
2015
10.1109/TNNLS.2014.2361026
IEEE Trans. Neural Netw. Learning Syst.
Keywords
Field
DocType
markov sampling,uniformly ergodic markov chain,online support vector machine (svm) classification,pattern classification,classification learning algorithm,generalization ability,markov processes,random sampling,kernel hilbert space,uniformly ergodic markov chain (u.e.m.c.),online svm classification,generalisation (artificial intelligence),sampling methods,support vector machines,uniformly ergodic markov chain (u.e.m.c.).,algorithm design and analysis,classification algorithms
Structured support vector machine,Maximum-entropy Markov model,Pattern recognition,Markov model,Computer science,Support vector machine,Markov chain,Markov blanket,Variable-order Markov model,Artificial intelligence,Hidden Markov model,Machine learning
Journal
Volume
Issue
ISSN
26
3
2162-2388
Citations 
PageRank 
References 
8
0.45
16
Authors
6
Name
Order
Citations
PageRank
Jie Xu1438.19
Yuan Yan Tang22662209.20
Bin Zou313013.96
Zongben Xu43203198.88
Luoqing Li547340.70
Yang Lu6636.04