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

fYear

1999

fDate

36373

Firstpage

12

Lastpage

20

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/NNSP.1999.788118

Filename

788118