TY  - JOUR
AU  - Eitrich, T.
AU  - Lang, B.
TI  - Efficient Optimization of Support Vector Machine Learning Parameters for Unbalanced Data Sets
JO  - Journal of Computational and Applied Mathematics
VL  - 196
SN  - 0377-0427
CY  - Amsterdam [u.a.]
PB  - North-Holland
M1  - PreJuSER-48496
SP  - 425 - 436
PY  - 2006
N1  - Record converted from VDB: 12.11.2012
AB  - Support 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.
KW  - J (WoSType)
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000239746800007
DO  - DOI:10.1016/j.cam.2005.09.009
UR  - https://juser.fz-juelich.de/record/48496
ER  -