Stopping conditions for exact computation of leave-one-out error in support vector machines

Vojtěch Franc, Pavel Laskov, Klaus Muller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

We propose a new stopping condition for a Support Vector Machine (SVM) solver which precisely reflects the objective of the Leave-One-Out error computation. The stopping condition guarantees that the output on an intermediate SVM solution is identical to the output of the optimal SVM solution with one data point excluded from the training set. A simple augmentation of a general SVM training algorithm allows one to use a stopping criterion equivalent to the proposed sufficient condition. A comprehensive experimental evaluation of our method shows consistent speedup of the exact LOO computation by our method, up to the factor of 13 for the linear kernel. The new algorithm can be seen as an example of constructive guidance of an optimization algorithm towards achieving the best attainable expected risk at optimal computational cost.

Original languageEnglish
Title of host publicationProceedings of the 25th International Conference on Machine Learning
Pages328-335
Number of pages8
Publication statusPublished - 2008 Nov 26
Externally publishedYes
Event25th International Conference on Machine Learning - Helsinki, Finland
Duration: 2008 Jul 52008 Jul 9

Other

Other25th International Conference on Machine Learning
CountryFinland
CityHelsinki
Period08/7/508/7/9

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Software

Cite this

Franc, V., Laskov, P., & Muller, K. (2008). Stopping conditions for exact computation of leave-one-out error in support vector machines. In Proceedings of the 25th International Conference on Machine Learning (pp. 328-335)