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. © Springer-Verlag Berlin Heidelberg 2005.
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CITATION STYLE
Chen, P. H., Fan, R. E., & Lin, C. J. (2005). Training support vector machines via SMO-type decomposition methods. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3735 LNAI, p. 15). Springer Verlag. https://doi.org/10.1007/11563983_3
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