Document Detail


Statistical properties of support vector machines with forgetting factor.
MedLine Citation:
PMID:  22154353     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Introducing a forgetting factor allows a support vector machine to solve time-varying problems adaptively. However, the exponential forgetting factor proposed in an earlier work does not ensure convergence of average generalization error even for a simple linearly separable problem. To guarantee convergence, we propose a factorial forgetting factor which decays factorially over time. We approximately derive the average generalization error of the factorial forgetting factor as well as that of the exponential forgetting factor using a simple one-dimensional problem, and confirm our theory by computer simulations. Finally, we show that our theory can be extended to arbitrary types of forgetting factors for simple linearly separable cases.
Authors:
Hiroyuki Funaya; Kazushi Ikeda
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-3-12
Journal Detail:
Title:  Neural networks : the official journal of the International Neural Network Society     Volume:  -     ISSN:  1879-2782     ISO Abbreviation:  -     Publication Date:  2011 Mar 
Date Detail:
Created Date:  2011-12-13     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8805018     Medline TA:  Neural Netw     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2011 Elsevier Ltd. All rights reserved.
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