Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/7667| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Prof. LEUNG Kwong Sak | en_US |
| dc.contributor.author | King Irwin | en_US |
| dc.contributor.author | Tse Ming-Fun | en_US |
| dc.date.accessioned | 2023-03-29T07:21:12Z | - |
| dc.date.available | 2023-03-29T07:21:12Z | - |
| dc.date.issued | 1999 | - |
| dc.identifier.citation | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 1999, vol. 5, pp. V-178 - V-184 | en_US |
| dc.identifier.issn | 08843627 | - |
| dc.identifier.uri | http://hdl.handle.net/20.500.11861/7667 | - |
| dc.description.abstract | This 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.iso | en | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.ispartof | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics | en_US |
| dc.title | FF99: a novel fuzzy first-order logic learning system | en_US |
| dc.type | Conference Proceedings | en_US |
| item.fulltext | No Fulltext | - |
| crisitem.author.dept | Department of Applied Data Science | - |
| Appears in Collections: | Publication | |
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