Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7667
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dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.contributor.authorKing Irwinen_US
dc.contributor.authorTse Ming-Funen_US
dc.date.accessioned2023-03-29T07:21:12Z-
dc.date.available2023-03-29T07:21:12Z-
dc.date.issued1999-
dc.identifier.citationProceedings of the IEEE International Conference on Systems, Man and Cybernetics, 1999, vol. 5, pp. V-178 - V-184en_US
dc.identifier.issn08843627-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7667-
dc.description.abstractThis paper describes a novel learning system, named FF99 that learns fuzzy first-order logic concepts from various kinds of data. FF99 builds on the ideas from both fuzzy set theory and first-order logic. Object relationships are described using fuzzy relations based on which FF99 generates classification rules expressed in a restricted from fuzzy first-order logic. This new system has been applied successfully to several tasks taken from the machine learning literature. We demonstrate its usefulness in the applications of data mining through several experiments.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofProceedings of the IEEE International Conference on Systems, Man and Cyberneticsen_US
dc.titleFF99: a novel fuzzy first-order logic learning systemen_US
dc.typeConference Proceedingsen_US
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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