Tong Zhang, Rie Kubota Ando
NeurIPS 2005
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.
Tong Zhang, Rie Kubota Ando
NeurIPS 2005
Christoph Tillmann, Tong Zhang
ACM Transactions on Speech and Language Processing
Radu Florian, Abe Ittycheriah, et al.
CoNLL 2003
Christoph Tillmann, Tong Zhang
ACL 2005