New Approaches to Statistical Learning Theory

Bousquet, O

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New Approaches to Statistical Learning Theory

Bousquet, O. (2003). New Approaches to Statistical Learning Theory. Annals of the Institute of Statistical Mathematics, 55(2), 371-389.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-DD72-D Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-DD73-B

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Bousquet, O¹, Author

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1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795

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Abstract: We present new tools from probability theory that can be applied to the analysis of learning algorithms. These tools allow to derive new bounds on the generalization performance of learning algorithms and to propose alternative measures of the complexity of the learning task, which in turn can be used to derive new learning algorithms.

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Dates: Date issued: 2003

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Identifiers: BibTex Citekey: 1996

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Title: Annals of the Institute of Statistical Mathematics

Source Genre: Journal

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Pages: - Volume / Issue: 55 (2) Sequence Number: - Start / End Page: 371 - 389 Identifier: -