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000048496 084__ $$2WoS$$aMathematics, Applied
000048496 1001_ $$0P:(DE-Juel1)VDB23445$$aEitrich, T.$$b0$$uFZJ
000048496 245__ $$aEfficient Optimization of Support Vector Machine Learning Parameters for Unbalanced Data Sets
000048496 260__ $$aAmsterdam [u.a.]$$bNorth-Holland$$c2006
000048496 300__ $$a425 - 436
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000048496 440_0 $$015061$$aJournal of Computational and Applied Mathematics$$v196$$x0377-0427$$y2
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000048496 520__ $$aSupport vector machines are powerful kernel methods for classification and regression tasks. If trained optimally, they produce excellent separating hyperplanes. The quality of the training, however, depends not only on the given training data but also on additional learning parameters, which are difficult to adjust, in particular for unbalanced datasets. Traditionally, grid search techniques have been used for determining suitable values for these parameters. In this paper, we propose an automated approach to adjusting the learning parameters using a derivative-free numerical optimizer. To make the optimization process more efficient, a new sensitive quality measure is introduced. Numerical tests with a well-known dataset show that our approach can produce support vector machines that are very well tuned to their classification tasks. (c) 2005 Elsevier B.V. All rights reserved.
000048496 536__ $$0G:(DE-Juel1)FUEK411$$2G:(DE-HGF)$$aScientific Computing$$cP41$$x0
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000048496 65320 $$2Author$$asupport vector machine
000048496 65320 $$2Author$$aparameter tuning
000048496 65320 $$2Author$$aunbalanced datasets
000048496 65320 $$2Author$$aderivative-free optimization
000048496 7001_ $$0P:(DE-HGF)0$$aLang, B.$$b1
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000048496 8567_ $$uhttp://dx.doi.org/10.1016/j.cam.2005.09.009
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000048496 9201_ $$0I:(DE-Juel1)VDB62$$d31.12.2007$$gZAM$$kZAM$$lZentralinstitut für Angewandte Mathematik$$x0
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