Document (#20659)

Author
Chen, H.
Title
Machine learning for information retrieval : neural networks, symbolic learning, and genetic algorithms
Source
Journal of the American Society for Information Science. 46(1995) no.3, S.194-216
Year
1994
Abstract
In the 1980s, knowledge-based techniques also made an impressive contribution to 'intelligent' information retrieval and indexing. More recently, researchers have turned to newer artificial intelligence based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms grounded on diverse paradigms. These have provided great opportunities to enhance the capabilities of current information storage and retrieval systems. Provides an overview of these techniques and presents 3 popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evaluation based genetic algorithms in the context of information retrieval. The techniques are promising in their ability to analyze user queries, identify users' information needs, and suggest alternatives for search and can greatly complement the prevailing full text, keyword based, probabilistic, and knowledge based techniques
Object
Hopfield-Netze
Aid
Hopfield-Netze

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  5. Cortez, E.M.; Park, S.C.; Kim, S.: ¬The hybrid application of an inductive learning method and a neural network (1995) 0.26
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