Machine learning of higher order programs

Ganesh Baliga, John Case, Sanjay Jain, Mandayam Suraj

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

A generator program for a computable function (by definition) generates an infinite sequence of programs all but finitely many of which compute that function. Machine learning of generator programs for computable functions is studied. To partially motivate these studies, it is shown that, in some cases, interesting global properties for computable functions can be proved from suitable generator programs which can not be proved from any ordinary programs for them. The power (for variants of various learning criteria from the literature) of learning generator programs is compared with the power of learning ordinary programs. The learning power in these cases is also compared to that of learning limiting programs, i.e., programs allowed finitely many mind changes about their correct outputs.

Original languageEnglish (US)
Title of host publicationLogical Foundations of Computer Science — Tver 1992 - 2nd International Symposium, Proceedings
EditorsAnil Nerode, Mikhail Taitslin
PublisherSpringer Verlag
Pages9-20
Number of pages12
ISBN (Print)9783540557074
DOIs
StatePublished - 1992
Event2nd International Symposia on Logical Foundations of Computer Science, Tver 1992 - Tver, Russian Federation
Duration: Jul 20 1992Jul 24 1992

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume620 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Symposia on Logical Foundations of Computer Science, Tver 1992
CountryRussian Federation
CityTver
Period7/20/927/24/92

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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