| Window-based example selection in learning vector quantization. | |
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MedLine Citation:
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PMID: 20804387 Owner: NLM Status: In-Process |
Abstract/OtherAbstract:
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A variety of modifications have been employed to learning vector quantization (LVQ) algorithms using either crisp or soft windows for selection of data. Although these schemes have been shown in practice to improve performance, a theoretical study on the influence of windows has so far been limited. Here we rigorously analyze the influence of windows in a controlled environment of gaussian mixtures in high dimensions. Concepts from statistical physics and the theory of online learning allow an exact description of the training dynamics, yielding typical learning curves, convergence properties, and achievable generalization abilities. We compare the performance and demonstrate the advantages of various algorithms, including LVQ 2.1, generalized LVQ (GLVQ), Learning from Mistakes (LFM) and Robust Soft LVQ (RSLVQ). We find that the selection of the window parameter highly influences the learning curves but not, surprisingly, the asymptotic performances of LVQ 2.1 and RSLVQ. Although the prototypes of LVQ 2.1 exhibit divergent behavior, the resulting decision boundary coincides with the optimal decision boundary, thus yielding optimal generalization ability. |
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Authors:
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A W Witoelar; A Ghosh; J J G de Vries; B Hammer; M Biehl |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Neural computation Volume: 22 ISSN: 1530-888X ISO Abbreviation: Neural Comput Publication Date: 2010 Nov |
Date Detail:
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Created Date: 2010-10-13 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9426182 Medline TA: Neural Comput Country: United States |
Other Details:
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Languages: eng Pagination: 2924-61 Citation Subset: IM |
Affiliation:
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Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Groningen, Netherlands. a.w.witoelar@rug.nl |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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