Training SVMs without offset

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We develop, analyze, and test a training algorithm for support vector machine cla.'>sifiers without offset. Key features of this algorithm are a new stopping criterion and a set of working set selection strategies that, although inexpensive, do not lead to substantially more iterations than the optimal working set selection strategy. For these working set strategies, we establish convergence rates that coincide with the best known rates for SYMs with offset. We further conduct various experiments that investigate both the run time behavior and the performed iterations of the new training algorithm. It turns out, that the new algorithm needs less ... continued below

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Steinwart, Ingo; Hush, Don & Scovel, Clint January 1, 2009.

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We develop, analyze, and test a training algorithm for support vector machine cla.'>sifiers without offset. Key features of this algorithm are a new stopping criterion and a set of working set selection strategies that, although inexpensive, do not lead to substantially more iterations than the optimal working set selection strategy. For these working set strategies, we establish convergence rates that coincide with the best known rates for SYMs with offset. We further conduct various experiments that investigate both the run time behavior and the performed iterations of the new training algorithm. It turns out, that the new algorithm needs less iterations and run-time than standard training algorithms for SYMs with offset.

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  • Journal Name: Journal of Machine Learning Research

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  • Report No.: LA-UR-09-00638
  • Report No.: LA-UR-09-638
  • Grant Number: AC52-06NA25396
  • Office of Scientific & Technical Information Report Number: 956365
  • Archival Resource Key: ark:/67531/metadc930657

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Office of Scientific & Technical Information Technical Reports

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  • January 1, 2009

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  • Nov. 13, 2016, 7:26 p.m.

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  • Dec. 12, 2016, 5:10 p.m.

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Steinwart, Ingo; Hush, Don & Scovel, Clint. Training SVMs without offset, article, January 1, 2009; [New Mexico]. (digital.library.unt.edu/ark:/67531/metadc930657/: accessed October 23, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.