Statistics Papers

Document Type

Conference Paper

Date of this Version

10-11-2003

Publication Source

44th Annual IEEE Symposium on Foundations of Computer Science

Start Page

189

Last Page

198

DOI

10.1109/SFCS.2003.1238193

Abstract

We consider a model of learning Boolean functions from examples generated by a uniform random walk on {0, 1}n. We give a polynomial time algorithm for learning decision trees and DNF formulas in this model. This is the first efficient algorithm for learning these classes in a natural passive learning model where the learner has no influence over the choice of examples used for learning.

Keywords

Boolean functions, Fourier analysis, computational complexity, decision trees, learning (artificial intelligence), Boolean function, DNF, disjunctive normal form, learning decision tree, passive learning model, polynomial time algorithm, random walk, algorithm design and analysis, computer science, decision trees, humans, knowledge representation, learning systems, mathematics, polynomials, statistics

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Date Posted: 27 November 2017