| Training a support vector machine in the primal. | |
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MedLine Citation:
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PMID: 17381263 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Most literature on support vector machines (SVMs) concentrates on the dual optimization problem. In this letter, we point out that the primal problem can also be solved efficiently for both linear and nonlinear SVMs and that there is no reason for ignoring this possibility. On the contrary, from the primal point of view, new families of algorithms for large-scale SVM training can be investigated. |
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Authors:
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Olivier Chapelle |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Neural computation Volume: 19 ISSN: 0899-7667 ISO Abbreviation: Neural Comput Publication Date: 2007 May |
Date Detail:
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Created Date: 2007-03-26 Completed Date: 2007-06-26 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: 1155-78 Citation Subset: IM |
Affiliation:
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Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany. olivier.chapelle@tuebingen.mpg.de |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Models, Theoretical Neural Networks (Computer)* Nonlinear Dynamics* Pattern Recognition, Automated* |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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