Geodesic
Adaptive constraint reduction for training support vector machines
Electronic transactions on numerical analysis, Tome 31 (2008), pp. 156-177.

Voir la notice de l'article provenant de la source Electronic Library of Mathematics

Summary: A support vector machine (SVM) determines whether a given observed pattern lies in a particular class. The decision is based on prior training of the SVM on a set of patterns with known classification, and training is achieved by solving a convex quadratic programming problem. Since there are typically a large number of training patterns, this can be expensive. In this work, we propose an adaptive constraint reduction primal-dual interior-point
Keywords: constraint reduction, column generation, primal-dual interior-point method, support vector machine 
@article{ETNA_2008__31__a12,
     author = {Jung, Jin Hyuk and O'Leary, Dianne P. and Tits, Andr\'e L.},
     title = {Adaptive constraint reduction for training support vector machines},
     journal = {Electronic transactions on numerical analysis},
     pages = {156--177},
     publisher = {mathdoc},
     volume = {31},
     year = {2008},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/ETNA_2008__31__a12/}
}
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Jung, Jin Hyuk; O'Leary, Dianne P.; Tits, André L. Adaptive constraint reduction for training support vector machines. Electronic transactions on numerical analysis, Tome 31 (2008), pp. 156-177. http://geodesic.mathdoc.fr/item/ETNA_2008__31__a12/