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2002•Unpublished venueRequires access

Exact learning via the Monotone theory

Nader H. Bshouty

Open publisher page 113 citations

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.>

About this research paper

What this paper is about

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.>

Why it matters

OpenAlex reports 113 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Available 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.>

Key concepts: Learnability, Boolean function, Equivalence (formal languages), Monotone polygon, Mathematics, Function (biology), Discrete mathematics, Decision tree

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