Year: 1992
Type: conference-paper
Abstract: Machine learning of limit programs (i.e., programs allowed finitely many mind changes about their legitimate outputs) for computable functions is studied. Learning of iterated limit programs is also studied. To partially motivate these studies, it is shown that, in some cases, interesting global properties of computable functions can be proved from suitable (n+1)-iterated limit programs for them which can not be proved from any n-iterated limit programs for them. It is shown that learning power is increased when (n+1)-iterated limit programs rather than n-iterated limit programs are to be learned. Many tradeoff results are obtained regarding learning power, number (possibly zero) of limits taken, program size constraints, and number of errors tolerated in final programs learned.
Source: Conference on Learning Theory
Authors: John Case, Sanjay Jain, Arun Sharma
Institutions: University of Delaware, UNSW Sydney
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W1983011418