Journal of Symbolic Logic

Machine Learning of Higher-Order Programs

Ganesh Baliga, John Case, Sanjay Jain, and Mandayam Suraj

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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.

Article information

Source
J. Symbolic Logic, Volume 59, Issue 2 (1994), 486-500.

Dates
First available in Project Euclid: 6 July 2007

Permanent link to this document
https://projecteuclid.org/euclid.jsl/1183744492

Mathematical Reviews number (MathSciNet)
MR1276627

Zentralblatt MATH identifier
0814.03034

JSTOR
links.jstor.org

Citation

Baliga, Ganesh; Case, John; Jain, Sanjay; Suraj, Mandayam. Machine Learning of Higher-Order Programs. J. Symbolic Logic 59 (1994), no. 2, 486--500. https://projecteuclid.org/euclid.jsl/1183744492


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