Learning Monotone Boolean Functions by Uniformly Distributed Examples

Abstract

Valiant introduced a new computational model of concept learning by examples, gave the definition of learnability of classes of Boolean functions, and derived algorithms for learning specific classes of Boolean functions. Using his model as a base, the authors show that the class of Boolean functions expressed by monotone disjunctive normal form formulae with at most a fixed number of monomials and the class of Boolean threshold functions are polynomial time learnable when the examples are generated according to the uniform distribution.