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Exact learning via the Monotone theory

Bshouty, N.H.  
Dept. of Comput. Sci., Calgary Univ., Alta.;

This paper appears in: Foundations of Computer Science, 1993. Proceedings., 34th Annual Symposium on
Publication Date: 3-5 Nov 1993
On page(s): 302-311
Meeting Date: 11/03/1993 - 11/05/1993
Location: Palo Alto, CA, USA
ISBN: 0-8186-4370-6
References Cited: 27
INSPEC Accession Number: 4851333
Digital Object Identifier: 10.1109/SFCS.1993.366857
Posted online: 2002-08-06 18:49:13.0

Abstract
We study the learnability of concept classes from membership and equivalence queries. We develop the Monotone theory that proves (1) Any boolean function is learnable as decision tree. (2) Any boolean function is either learnable as DNF or as CNF (or both). The first result solves the open problem of the learnability of decision trees and the second result gives more evidence that DNFs are not “very hard” to learn

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