Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.ic.2004.03.003
Title: Learning all subfunctions of a function
Authors: Jain, S. 
Kinber, E.
Wiehagen, R.
Issue Date: 2004
Source: Jain, S.,Kinber, E.,Wiehagen, R. (2004). Learning all subfunctions of a function. Information and Computation 192 (2) : 185-215. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ic.2004.03.003
Abstract: Sublearning, a model for learning of subconcepts of a concept, is presented. Sublearning a class of total recursive functions informally means to learn all functions from that class together with all of their subfunctions. While in language learning it is known to be impossible to learn any infinite language together with all of its sublanguages, the situation changes for sublearning of functions. Several types of sublearning are defined and compared to each other as well as to other learning types. For example, in some cases, sublearning coincides with robust learning. Furthermore, whereas in usual function learning there are classes that cannot be learned consistently, all sublearnable classes of some natural types can be learned consistently. Moreover, the power of sublearning is characterized in several terms, thereby establishing a close connection to measurable classes and variants of this notion. As a consequence, there are rich classes which do not need any self-referential coding for sublearning them. © 2004 Elsevier Inc. All rights reserved.
Source Title: Information and Computation
URI: http://scholarbank.nus.edu.sg/handle/10635/39116
ISSN: 08905401
DOI: 10.1016/j.ic.2004.03.003
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

2
checked on Dec 13, 2017

WEB OF SCIENCETM
Citations

2
checked on Nov 2, 2017

Page view(s)

49
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.