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  Improving the accuracy and speed of support vector learning machines

Burges, C., & Schölkopf, B. (1997). Improving the accuracy and speed of support vector learning machines. In M. Mozer, M. Jordan, & T. Petsche (Eds.), Advances in Neural Information Processing Systems 9 (pp. 375-381). Cambridge, MA, USA: MIT Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-EA22-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-E307-6
Genre: Conference Paper

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 Creators:
Burges, CJC, Author
Schölkopf, B1, 2, Author              
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Support Vector Learning Machines (SVM) are finding application in pattern recognition, regression estimation, and operator inversion for illposed problems. Against this very general backdrop any methods for improving the generalization performance, or for improving the speed in test phase of SVMs are of increasing interest. In this paper we combine two such techniques on a pattern recognition problem The method for improving generalization performance the "virtual support vector" method does so by incorporating known invariances of the problem This method achieves a drop in the error rate on 10.000 NIST test digit images of 1,4 to 1 . The method for improving the speed (the "reduced set" method) does so by approximating the support vector decision surface. We apply this method to achieve a factor of fifty speedup in test phase over the virtual support vector machine The combined approach yields a machine which is both 22 times faster than the original machine, and which has better generalization performance achieving 1,1 error. The virtual support vector method is applicable to any SVM problem with known invariances The reduced set method is applicable to any support vector machine.

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 Dates: 1997-05
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: 438
 Degree: -

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Title: Tenth Annual Conference on Neural Information Processing Systems (NIPS 1996)
Place of Event: Denver, CO, USA
Start-/End Date: 1996-12-02 - 1996-12-05

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Title: Advances in Neural Information Processing Systems 9
Source Genre: Proceedings
 Creator(s):
Mozer, ME, Editor
Jordan, MI, Editor
Petsche, T, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 375 - 381 Identifier: ISBN: 0-262-10065-7