Title :
Margin-like quantities and generalized approximate cross validation for support vector machines
Author :
Wahba, Grace ; Lin, Yi ; Zhang, Hao
Author_Institution :
Dept. of Stat., Wisconsin Univ., Madison, WI, USA
Abstract :
We examine support vector machines (SVM) from the point of view of solutions to variational problems in a reproducing kernel Hilbert space. We discuss the generalized comparative Kullback-Leibler distance as a target for choosing tuning parameters in SVMs, and we propose that the generalized approximate cross validation estimate of them is a reasonable proxy for this target. We indicate an interesting relationship between the generalized approximate cross validation and the SVM margin
Keywords :
Hilbert spaces; learning (artificial intelligence); neural nets; pattern classification; set theory; tensors; variational techniques; generalized approximate cross validation; generalized approximate cross validation estimate; generalized comparative Kullback-Leibler distance; margin-like quantities; reproducing kernel Hilbert space; support vector machines; tuning parameters; variational problems; Hilbert space; Kernel; Mathematical programming; Smoothing methods; Spline; Statistics; Support vector machine classification; Support vector machines; Upper bound; Virtual colonoscopy;
Conference_Titel :
Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE Signal Processing Society Workshop.
Conference_Location :
Madison, WI
Print_ISBN :
0-7803-5673-X
DOI :
10.1109/NNSP.1999.788118
