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  Stability and Generalization

Bousquet, O. (2002). Stability and Generalization. Journal of Machine Learning Research, 2, 499-526.

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Bousquet, O1, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: 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.

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 Dates: 2002
 Publication Status: Issued
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 Identifiers: BibTex Citekey: 1439
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Title: Journal of Machine Learning Research
Source Genre: Journal
 Creator(s):
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Pages: - Volume / Issue: 2 Sequence Number: - Start / End Page: 499 - 526 Identifier: -