USING CONCEPT LEARNING FOR KNOWLEDGE ACQUISITION
| dc.contributor.author | MacDonald, Bruce A. | eng |
| dc.contributor.author | Witten, Ian H. | eng |
| dc.date.accessioned | 2008-02-26T22:37:29Z | |
| dc.date.available | 2008-02-26T22:37:29Z | |
| dc.date.computerscience | 1999-05-27 | eng |
| dc.date.issued | 1987-09-01 | eng |
| dc.description.abstract | Although experts have difficulty formulating their knowledge explicitly as rules, they find it easy to demonstrate their expertise in specific situations. Schemes for learning concepts from examples offer the potential for domain experts to interact directly with machines to transfer knowledge. Concept learning methods divide into similarity-based, hierachical, function induction, and explanation-based knowledge-intensive techniques. These are described, classified according to input and output representations, and related to knowledge acquisition for expert systems. Systems discussed include candidate elimination, version space, ID3, PRISM, MARVIN, NODDY, BACON, COPER, and LEX-II. Teaching requirements are also analyzed. | eng |
| dc.description.notes | We are currently acquiring citations for the work deposited into this collection. We recognize the distribution rights of this item may have been assigned to another entity, other than the author(s) of the work.If you can provide the citation for this work or you think you own the distribution rights to this work please contact the Institutional Repository Administrator at digitize@ucalgary.ca | eng |
| dc.identifier.department | 1987-278-26 | eng |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/30901 | |
| dc.identifier.uri | http://hdl.handle.net/1880/45567 | |
| dc.language.iso | Eng | eng |
| dc.publisher.corporate | University of Calgary | eng |
| dc.publisher.faculty | Science | eng |
| dc.subject | Computer Science | eng |
| dc.title | USING CONCEPT LEARNING FOR KNOWLEDGE ACQUISITION | eng |
| dc.type | unknown | |
| thesis.degree.discipline | Computer Science | eng |