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 language | English |
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Title of host publication | Computational Learning Theory - 2nd European Conference, EuroCOLT 1995, Proceedings |
Editors | Paul Vitanyi |
Publisher | Springer Verlag |
Pages | 357-366 |
Number of pages | 10 |
ISBN (Print) | 9783540591191 |
DOIs | |
State | Published - 1995 |
Event | 2nd European Conference on Computational Learning Theory, EuroCOLT 1995 - Barcelona, Spain Duration: 13 Mar 1995 → 15 Mar 1995 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 904 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 2nd European Conference on Computational Learning Theory, EuroCOLT 1995 |
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Country/Territory | Spain |
City | Barcelona |
Period | 13/03/95 → 15/03/95 |
Bibliographical note
Publisher Copyright:© Springer-Verlag Berlin Heidelberg 1995.