| Statistical properties of support vector machines with forgetting factor. | |
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MedLine Citation:
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PMID: 22154353 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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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. |
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Authors:
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Hiroyuki Funaya; Kazushi Ikeda |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-3-12 |
Journal Detail:
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Title: Neural networks : the official journal of the International Neural Network Society Volume: - ISSN: 1879-2782 ISO Abbreviation: - Publication Date: 2011 Mar |
Date Detail:
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Created Date: 2011-12-13 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8805018 Medline TA: Neural Netw Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Copyright Information:
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Copyright © 2011 Elsevier Ltd. All rights reserved. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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