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  Stability and Generalization

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

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-E0C2-2 Version Permalink: https://hdl.handle.net/21.11116/0000-0005-87BB-0
Genre: Journal Article

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http://www.jmlr.org/papers/volume2/bousquet02a/bousquet02a.pdf (Publisher version)
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 Creators:
Bousquet, O1, Author           
Elisseeff, A1, Author           
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1External Organizations, ou_persistent22              

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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: 2002-03
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: BibTex Citekey: 1439
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Title: The Journal of Machine Learning Research
Source Genre: Journal
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Publ. Info: Cambridge, MA : MIT Press
Pages: - Volume / Issue: 2 Sequence Number: - Start / End Page: 499 - 526 Identifier: ISSN: 1532-4435
CoNE: https://pure.mpg.de/cone/journals/resource/111002212682020_1