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Authors: Pai-Hsuen Chen, Rong-En Fan, and Chih-Jen Lin
Affiliation: Department of Computer Science and Information Engineering,
National Taiwan University, Taipei, Taiwan
Source: Algorithmic Learning Theory, 16th International Conference,
ALT 2005, Singapore, October 2005, Proceedings,
(Sanjay Jain, Hans Ulrich Simon and Etsuji Tomita, Eds.),
Lecture Notes in Artificial Intelligence 3734, pp. 45 - 62, Springer 2005.
Abstract.
This article gives a comprehensive study on SMO-type (Sequential Minimal Optimization)
decomposition methods for training support vector machines. We propose a general and
flexible selection of the two-element working set. Main theoretical results include 1)
a simple asymptotic convergence proof, 2) a useful explanation of the shrinking and
caching techniques, and 3) the linear convergence of this method. This analysis
applies to any SMO-type implementation whose selection falls into the proposed
framework.
©Copyright 2005 Springer
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