@inproceedings{c9de3cefc2d64e57ae8b476ab9dabc9c,
title = "Machine learning of higher order programs",
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.",
author = "Ganesh Baliga and John Case and Sanjay Jain and Mandayam Suraj",
note = "Publisher Copyright: {\textcopyright} 1992, Springer Verlag. All rights reserved.; 2nd International Symposia on Logical Foundations of Computer Science, Tver 1992 ; Conference date: 20-07-1992 Through 24-07-1992",
year = "1992",
doi = "10.1007/BFb0023859",
language = "English (US)",
isbn = "9783540557074",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "9--20",
editor = "Anil Nerode and Mikhail Taitslin",
booktitle = "Logical Foundations of Computer Science — Tver 1992 - 2nd International Symposium, Proceedings",
address = "Germany",
}