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Machine learning of higher-order programs

Published online by Cambridge University Press:  12 March 2014

Ganesh Baliga ,
John Case ,
Sanjay Jain  and
Mandayam Suraj
Ganesh Baliga
Affiliation:
Department of Computer and Information Sciences, University of Delaware, Newark, Delaware 19176, E-mail: baliga@cis.udel.edu
John Case
Affiliation:
Department of Computer and Information Sciences, University of Delaware, Newark, Delaware 19176, E-mail: case@cis.udel.edu
Sanjay Jain
Affiliation:
Institute of Systems Science, National University of Singapore, Singapore 0511, E-mail: sanjay@iss.nus.sg
Mandayam Suraj
Affiliation:
Department of Computer and Information Sciences, University of Delaware, Newark, Delaware 19176, E-mail: suraj@cis.udel.edu
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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 motivate these studies partially, it is shown that, in some cases, interesting global properties for computable functions can be proved from suitable generator programs which cannot 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.

Information

Type
Research Article
Information
The Journal of Symbolic Logic , Volume 59 , Issue 2 , June 1994 , pp. 486 - 500
Copyright
Copyright © Association for Symbolic Logic 1994

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