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  Algorithmic Stability and Generalization Performance

Bousquet, O., & Elisseeff, A. (2001). Algorithmic Stability and Generalization Performance. In T. Leen, T. Dietterich, & V. Tresp (Eds.), Advances in Neural Information Processing Systems 13 (pp. 196-202). Cambridge, MA, USA: MIT Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E2AA-C Version Permalink: http://hdl.handle.net/21.11116/0000-0005-0EFD-0
Genre: Conference Paper

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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 present a novel way of obtaining PAC-style bounds on the generalization error of learning algorithms, explicitly using their stability properties. A \em stable learner being one for which the learned solution does not change much for small changes in the training set. The bounds we obtain do not depend on any measure of the complexity of the hypothesis space (e.g. VC dimension) but rather depend on how the learning algorithm searches this space, and can thus be applied even when the VC dimension in infinite. We demonstrate that regularization networks possess the required stability property and apply our method to obtain new bounds on their generalization performance.

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 Dates: 2001-04
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: 1437
 Degree: -

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Title: Fourteenth Annual Neural Information Processing Systems Conference (NIPS 2000)
Place of Event: Denver, CO, USA
Start-/End Date: 2000-11-27 - 2000-12-02

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Title: Advances in Neural Information Processing Systems 13
Source Genre: Proceedings
 Creator(s):
Leen, TK, Editor
Dietterich, TG, Editor
Tresp, V, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 196 - 202 Identifier: ISBN: 0-262-12241-3