کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4643349 | 1341377 | 2006 | 12 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Efficient optimization of support vector machine learning parameters for unbalanced datasets Efficient optimization of support vector machine learning parameters for unbalanced datasets](/preview/png/4643349.png)
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.
Journal: Journal of Computational and Applied Mathematics - Volume 196, Issue 2, 15 November 2006, Pages 425–436