Journal of Symbolic Logic 59 (2):486-500 (1994)

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
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DOI 10.2307/2275402
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References found in this work BETA

Theory of Recursive Functions and Effective Computability.Hartley Rogers - 1971 - Journal of Symbolic Logic 36 (1):141-146.
Introduction to Mathematical Logic.John Corcoran - 1964 - Journal of Symbolic Logic 54 (2):618-619.
An Introduction to the General Theory of Algorithms.Michael Machtey & Paul Young - 1981 - Journal of Symbolic Logic 46 (4):877-878.
Degrees of Unsolvability.Leonard P. Sasso - 1975 - Journal of Symbolic Logic 40 (3):452-453.

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