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

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dc.creatorWolff, J. G.
dc.date2003-02-12
dc.date.accessioned2026-07-07T03:19:26Z
dc.date.available2026-07-07T03:19:26Z
dc.descriptionThis 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.
dc.description39 pages, 1 JPEG figure
dc.identifierhttps://arxiv.org/abs/cs/0302015
dc.identifierhttp://arxiv.org/abs/cs/0302015
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31462
dc.subjectArtificial Intelligence
dc.subjectMachine Learning
dc.subjectI.2.4; I.2.6; I.2.7
dc.titleUnsupervised Learning in a Framework of Information Compression by Multiple Alignment, Unification and Search
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