Support vector machines: heuristic of alternatives

Details Support vector machines: heuristic of alternatives Support vector machines: heuristic of alternatives

Jan 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008spie.6937e.121o&link_type=abstract

Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2007. Edited by Romaniuk, Rys

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In this paper it will be presented Sequential Minimal Optimization (SMO) default heuristic optimization. SMO is an algorithm for solving Support Vector Machines (SVM) problem. SMO default heuristic chooses to the active set the worst two parameters based on the Karush-Kuhn-Tucker (KKT) conditions. The proposed heuristic of alternatives chooses parameters to the active set on the basis of not only KKT conditions, but also objective function value growth. Tests show that heuristic of alternatives is generally better than SMO default heuristic.

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