Stability and Generalization
2002
Article
ei
We define notions of stability for learning algorithms and show how to use these notions to derive generalization error bounds based on the empirical error and the leave-one-out error. The methods we use can be applied in the regression framework as well as in the classification one when the classifier is obtained by thresholding a real-valued function. We study the stability properties of large classes of learning algorithms such as regularization based algorithms. In particular we focus on Hilbert space regularization and Kullback-Leibler regularization. We demonstrate how to apply the results to SVM for regression and classification.
Author(s): | Bousquet, O. and Elisseeff, A. |
Journal: | Journal of Machine Learning Research |
Volume: | 2 |
Pages: | 499-526 |
Year: | 2002 |
Day: | 0 |
Department(s): | Empirische Inferenz |
Bibtex Type: | Article (article) |
Digital: | 0 |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
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BibTex @article{1439, title = {Stability and Generalization}, author = {Bousquet, O. and Elisseeff, A.}, journal = {Journal of Machine Learning Research}, volume = {2}, pages = {499-526}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, year = {2002}, doi = {} } |