PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A General Dimension for Query Learning
José Balcázar, Jorge Castro, David Guijarro, Johannes Köbler and Wolfgang Lindner
Journal of Computer and System Sciences Volume 73, Number 6, pp. 924-940, 2007.

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Abstract

We introduce a combinatorial dimension that characterizes the number of queries needed to exactly (or approximately) learn concept classes in various models. Our general dimension provides tight upper and lower bounds on the query complexity for all sorts of queries, not only for example-based queries as in previous works. As an application we show that for learning DNF formulas, unspecified attribute value membership and equivalence queries are not more powerful than standard membership and equivalence queries. Further, in the approximate learning setting, we use the general dimension to characterize the query complexity in the statistical query as well as the learning by distances model. Moreover, we derive close bounds on the number of statistical queries needed to approximately learn DNF formulas.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:3314
Deposited By:Jorge Castro
Deposited On:07 February 2008

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