On exact learning monotone DNF from membership queries

Hasan Abasi, Nader H. Bshouty, Hanna Mazzawi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we study the problem of learning a monotone DNF with at most s terms of size (number of variables in each term) at most r (s term r-MDNF) from membership queries. This problem is equivalent to the problem of learning a general hypergraph using hyperedge-detecting queries, a problem motivated by applications arising in chemical reactions and genome sequencing.

We first present new lower bounds for this problem and then present deterministic and randomized adaptive algorithms with query complexities that are almost optimal. All the algorithms we present in this paper run in time linear in the query complexity and the number of variables n. In addition, all of the algorithms we present in this paper are asymptotically tight for fixed r and/or s.

Original languageEnglish
Title of host publicationAlgorithmic Learning Theory - 25th International Conference, ALT 2014, Proceedings
EditorsPeter Auer, Alexander Clark, Thomas Zeugmann, Sandra Zilles
Pages111-124
Number of pages14
ISBN (Electronic)9783319116617
DOIs
StatePublished - 2014
Event25th International Conference on Algorithmic Learning Theory, ALT 2014 - Bled, Slovenia
Duration: 8 Oct 201410 Oct 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8776

Conference

Conference25th International Conference on Algorithmic Learning Theory, ALT 2014
Country/TerritorySlovenia
CityBled
Period8/10/1410/10/14

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

Fingerprint

Dive into the research topics of 'On exact learning monotone DNF from membership queries'. Together they form a unique fingerprint.

Cite this