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Unsupervised learning in a framework of information compression by multiple alignment, unification and search (2003)
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| Venue: | Artificial Intelligence Review |
| Citations: | 8 - 6 self |
BibTeX
@TECHREPORT{Wolff03unsupervisedlearning,
author = {J Gerard Wolff},
title = {Unsupervised learning in a framework of information compression by multiple alignment, unification and search},
institution = {Artificial Intelligence Review},
year = {2003}
}
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Abstract
This paper describes a novel approach to unsupervised learning that has been developed within a framework designed to integrate learning with such things as parsing and production of language, fuzzy pattern recognition and best-match information retrieval, class hierarchies with inheritance of attributes, probabilistic and exact forms of reasoning, and others. This framework, which may be characterised as information compression by multiple alignment, unification and search (ICMAUS), is founded on principles of Minimum Length Encoding. Some of its capabilities (other than learning) are briefly described. The main body of the paper describes SP70, a computer model of the ICMAUS framework that incorporates processes for unsupervised learning. Examples are presented to show how the model can infer plausible grammars from appropriate input. Anticipated future developments of the model are briefly discussed.
Keyphrases
information compression multiple alignment unsupervised learning best-match information retrieval main body appropriate input icmaus framework minimum length encoding fuzzy pattern recognition exact form novel approach class hierarchy anticipated future development computer model plausible grammar
