Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/99352
Title: On a question about learning nearly minimal programs
Authors: Jain, S. 
Keywords: Inductive inference
Machine learning
Methodology of science
Minimal programs
Recursion theory
Theory of computation
Issue Date: 13-Jan-1995
Source: Jain, S. (1995-01-13). On a question about learning nearly minimal programs. Information Processing Letters 53 (1) : 1-4. ScholarBank@NUS Repository.
Abstract: Identification by algorithmic devices of programs for computable functions from their graphs is a well studied problem in learning theory. Freivalds and Chen consider identification of "minimal" and "nearly minimal" programs for functions from their graphs. The present paper solves the following question left open by Chen: Is it the case that for any collection of computable functions, C, such that some machine can finitely learn a nearly minimal (n + 1)-error program for every function in C, there exists another machine that can learn in the limit an n-error program (which need not be nearly minimal) for every function in C? We answer this question negatively. © 1995.
Source Title: Information Processing Letters
URI: http://scholarbank.nus.edu.sg/handle/10635/99352
ISSN: 00200190
Appears in Collections:Staff Publications

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