Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chang, KC | en_US |
dc.contributor.author | Hong, TP | en_US |
dc.contributor.author | Tseng, SS | en_US |
dc.date.accessioned | 2014-12-08T15:02:41Z | - |
dc.date.available | 2014-12-08T15:02:41Z | - |
dc.date.issued | 1996-05-01 | en_US |
dc.identifier.issn | 0924-6495 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/1328 | - |
dc.description.abstract | Learning general concepts in imperfect environments is difficult since training instances often include noisy data, inconclusive data, incomplete data, unknown attributes, unknown attribute values and other barriers to effective learning. It is well known that people can learn effectively in imperfect environments, and can manage to process very large amounts of data. Imitating human learning behavior therefore provides a useful model for machine learning in real-world applications. This paper proposes a new, more effective way to represent imperfect training instances and rules, and based on the new representation, a Human-Like Learning (HULL) algorithm for incrementally learning concepts well in imperfect training environments. Several examples are given to make the algorithm clearer. Finally, experimental results are presented that show the proposed learning algorithm works well in imperfect learning environments. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | machine learning | en_US |
dc.subject | human learning | en_US |
dc.subject | rule | en_US |
dc.subject | training instance | en_US |
dc.subject | specialize | en_US |
dc.subject | generalize | en_US |
dc.title | Machine learning by imitating human learning | en_US |
dc.type | Article | en_US |
dc.identifier.journal | MINDS AND MACHINES | en_US |
dc.citation.volume | 6 | en_US |
dc.citation.issue | 2 | en_US |
dc.citation.spage | 203 | en_US |
dc.citation.epage | 228 | en_US |
dc.contributor.department | 資訊工程學系 | zh_TW |
dc.contributor.department | Department of Computer Science | en_US |
dc.identifier.wosnumber | WOS:A1996VB19100004 | - |
dc.citation.woscount | 2 | - |
Appears in Collections: | Articles |
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