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Unsupervised Learning in a Framework of Information Compression by Multiple Alignment, Unification and Search

2003/02/12 by J. Gerard Wolff, J. G. Wolff, Wolff, J. G. · 1 citation
Computer Science · #Algorithms and Data Compression #Computability, Logic, AI Algorithms #Machine Learning and Algorithms #cs.AI #cs.LG

paper · pdf · doi:10.48550/arxiv.cs/0302015

39 pages, 1 JPEG figure

arxiv created 2003/02/12 · arxiv updated 2009/11/30

Abstract

This paper describes a novel approach to unsupervised learning that has been developed within a framework of "information compression by multiple alignment, unification and search" (ICMAUS), designed to integrate learning with other AI functions such as parsing and production of language, fuzzy pattern recognition, probabilistic and exact forms of reasoning, and others.

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