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Generalization ability of extreme learning machine with uniformly ergodic Markov chains
Yuan P.1; Chen H.1; Zhou Y.2; Deng X.1; Zou B.3
2015
Source PublicationNeurocomputing
ISSN18728286 09252312
Volume167Pages:528-534
Abstract

Extreme learning machine (ELM) has gained increasing attention for its computation feasibility on various applications. However, the previous generalization analysis of ELM relies on the independent and identically distributed (i.i.d) samples. In this paper, we go far beyond this restriction by investigating the generalization bound of the ELM classification associated with the uniform ergodic Markov chains (u.e. M.c) samples. The upper bound of the misclassification error is estimated for the ELM classification showing that the satisfactory learning rate can be achieved even for the dependent samples. Empirical evaluations on real-word datasets are provided to compare the predictive performance of ELM with independent and Markov sampling.

KeywordExtreme Learning Machine Generalization Ability Uniformly Ergodic Markov Chain
DOIhttp://doi.org/10.1016/j.neucom.2015.04.041
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000358808500055
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Cited Times [WOS]:2   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorChen H.; Deng X.
Affiliation1.Huazhong Agricultural University
2.Universidade de Macau
3.Hubei University
Recommended Citation
GB/T 7714
Yuan P.,Chen H.,Zhou Y.,et al. Generalization ability of extreme learning machine with uniformly ergodic Markov chains[J]. Neurocomputing,2015,167:528-534.
APA Yuan P.,Chen H.,Zhou Y.,Deng X.,&Zou B..(2015).Generalization ability of extreme learning machine with uniformly ergodic Markov chains.Neurocomputing,167,528-534.
MLA Yuan P.,et al."Generalization ability of extreme learning machine with uniformly ergodic Markov chains".Neurocomputing 167(2015):528-534.
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