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Machine Learning of Higher-Order Programs
Ganesh Baliga, John Case, Sanjay Jain and Mandayam Suraj
The Journal of Symbolic Logic
Vol. 59, No. 2 (Jun., 1994), pp. 486-500
Published by: Association for Symbolic Logic
Stable URL: http://www.jstor.org/stable/2275402
Page Count: 15
You can always find the topics here!Topics: Machine learning, Mathematical theorems, Inference, Peano axioms, Recursion, Mathematical functions, Mathematical monotonicity, Recursive functions, Computer aided software engineering, Diagonal arguments
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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.
The Journal of Symbolic Logic © 1994 Association for Symbolic Logic