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Abstract: Support vector machines are trained by solving constrained quadratic optimization problems. This is usually done with an iterative decomposition algorithm operating on a small working set of variables in every iteration. The training time strongly depends on the selection of these variables. We propose the maximum-gain working set selection algorithm for large scale quadratic programming. (Update)
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BibTeX entry: (Update)
@misc{ tobias-journal,
author = "Tobias Glasmachers Tobias",
title = "Journal of Machine Learning Research 7 (2006) 1437--1466 Submitted 9/05;
Revised 10/05; Published 6/06 Maximum-Gain Working Set Selection for SVMs",
url = "citeseer.ist.psu.edu/759619.html" }
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