Learning by extended statistical queries and its relation to PAC learning

Eli Shamir, Clara Shwartzman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

PAC learning from examples is factored so that (i) the membership queries axe used to evaluate empirically "statistical queries" -certain expectations of ftmctionals involving the unknown target, (ii) approximate value of these statistical queries are used to compute an output - an approximation of the target. K earns' original formulation of statistical queries [we use the abbreviation SQ], is extended here to include as SQ functionals of arbitrary range and order higher than one - second order being the most useful addition. This enables us to capture more ground for casting efficient PAC learning algorithms in SQ form: The important Kushilevitz-Mansour Fourier - based algorithm has an SQ rendition, as well as its derivatives, e.g. Jackson's recent DNF learning. Efficient evaluation of extended SQ by membership queries, if possible at all, becomes quite intricate. We show, however, that it is usually robust under classification noise.

Original languageEnglish
Title of host publicationComputational Learning Theory - 2nd European Conference, EuroCOLT 1995, Proceedings
EditorsPaul Vitanyi
PublisherSpringer Verlag
Pages357-366
Number of pages10
ISBN (Print)9783540591191
DOIs
StatePublished - 1995
Event2nd European Conference on Computational Learning Theory, EuroCOLT 1995 - Barcelona, Spain
Duration: 13 Mar 199515 Mar 1995

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume904
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd European Conference on Computational Learning Theory, EuroCOLT 1995
Country/TerritorySpain
CityBarcelona
Period13/03/9515/03/95

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1995.

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