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.
The full version of this paper is published in the Proceedings of Algorithmic Learning Theory, the 16th International Conference, ALT 2005, Lecture Notes in Artificial Intelligence Vol. 3734.
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© 2005 Springer-Verlag Berlin Heidelberg
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Chen, PH., Fan, RE., Lin, CJ. (2005). Training Support Vector Machines via SMO-Type Decomposition Methods. In: Hoffmann, A., Motoda, H., Scheffer, T. (eds) Discovery Science. DS 2005. Lecture Notes in Computer Science(), vol 3735. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11563983_3
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DOI: https://doi.org/10.1007/11563983_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29230-2
Online ISBN: 978-3-540-31698-5
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