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(Neural Computation. 2003;15:1397-1437.)
© 2003 The MIT Press

Letter

Leave-One-Out Bounds for Kernel Methods

Tong Zhang

tzhang{at}watson.ibm.com, IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, U.S.A.

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-one-out errors. We apply our analysis to some classification and regression problems and compare them with previous results.




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T. Zhang
Learning Bounds for Kernel Regression Using Effective Data Dimensionality
Neural Comput., September 1, 2005; 17(9): 2077 - 2098.
[Abstract] [Full Text] [PDF]


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