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Computational learning theory / Probably approximately correct learning / Sample size determination / PP / Central limit theorem / Statistical power / Expected value / Statistics / Theoretical computer science / Hypothesis testing


Sample-Efficient Strategies for Learning in the Presence of Noise ` CESA-BIANCHI NICOLO University of Milan, Milan, Italy
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Document Date: 2003-04-11 14:30:58

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City

Milano / Haifa / Bochum / /

Company

Notation We / IBM Haifa Research Laboratory / Definitions / ESPRIT / /

Country

Italy / /

Currency

USD / /

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Facility

University of Milan / University of Dortmund / Israel PAUL FISCHER University of Dortmund / Italy ELI DICHTERMAN IBM Haifa Research Laboratory / Hebrew University / Germany ELI SHAMIR Hebrew University / CESA-BIANCHI NICOLO University of Milan / /

IndustryTerm

realistic learning algorithm / randomized learning algorithms / generic algorithm / learning algorithm / learning algorithms / /

Organization

Hebrew University / Jerusalem / University of Dortmund / Department of Computer Science / University of Milan / Milan / Association for Computing Machinery / Israel PAUL FISCHER University of Dortmund / Dortmund / /

Person

HANS ULRICH SIMON / P. Fischer / H. Ulrich Simon / ELI DICHTERMAN / ELI SHAMIR / /

Position

General / /

ProgrammingLanguage

D / C / /

PublishedMedium

Journal of the ACM / Theory of Computing / /

Technology

learning algorithm / randomized learning algorithms / artificial intelligence / generic algorithm / realistic learning algorithm / following protocol / /

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