Conference paper
Named Entity Recognition through Classifier Combination
Radu Florian, Abe Ittycheriah, et al.
CoNLL 2003
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.
Radu Florian, Abe Ittycheriah, et al.
CoNLL 2003
Rie Johnson, Tong Zhang
JMLR
Honglei Guo, Jianmin Jiang, et al.
IJCNLP 2004
Tong Zhang
NeurIPS 2003