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Stability and Generalization

2002

Article

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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): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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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}
}