| Stochastic Subset Selection for Learning With Kernel Machines. | |
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MedLine Citation:
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PMID: 22049369 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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Kernel machines have gained much popularity in applications of machine learning. Support vector machines (SVMs) are a subset of kernel machines and generalize well for classification, regression, and anomaly detection tasks. The training procedure for traditional SVMs involves solving a quadratic programming (QP) problem. The QP problem scales super linearly in computational effort with the number of training samples and is often used for the offline batch processing of data. Kernel machines operate by retaining a subset of observed data during training. The data vectors contained within this subset are referred to as support vectors (SVs). The work presented in this paper introduces a subset selection method for the use of kernel machines in online, changing environments. Our algorithm works by using a stochastic indexing technique when selecting a subset of SVs when computing the kernel expansion. The work described here is novel because it separates the selection of kernel basis functions from the training algorithm used. The subset selection algorithm presented here can be used in conjunction with any online training technique. It is important for online kernel machines to be computationally efficient due to the real-time requirements of online environments. Our algorithm is an important contribution because it scales linearly with the number of training samples and is compatible with current training techniques. Our algorithm outperforms standard techniques in terms of computational efficiency and provides increased recognition accuracy in our experiments. We provide results from experiments using both simulated and real-world data sets to verify our algorithm. |
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Authors:
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Jason Rhinelander; Xiaoping P Liu |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-10-27 |
Journal Detail:
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Title: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society Volume: - ISSN: 1941-0492 ISO Abbreviation: - Publication Date: 2011 Oct |
Date Detail:
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Created Date: 2011-11-3 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9890044 Medline TA: IEEE Trans Syst Man Cybern B Cybern Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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