Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

June 1, 2003, Vol. 15, No. 6, Pages 1397-1437
(doi: 10.1162/089976603321780326)
© 2003 Massachusetts Institute of Technology
Leave-One-Out Bounds for Kernel Methods
Article PDF (247.91 KB)
Abstract

In this article, we study leave-one-out style cross-validation bounds for kernel methods. The essential element in our analysis is a bound on the parameter estimation stability for regularized kernel formulations. Using this result, we derive bounds on expected leave-one-out cross-validation errors, which lead to expected generalization bounds for various kernel algorithms. In addition, we also obtain variance bounds for leave-oneout errors. We apply our analysis to some classification and regression problems and compare them with previous results.