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- Suzanna Becker
- Department of Computer Science, University of Toronto Toronto, Ontario, M5S 1A4, Canada
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抄録
<jats:p> Supervised learning procedures for neural networks have recently met with considerable success in learning difficult mappings. However, their range of applicability is limited by their poor scaling behavior, lack of biological plausibility, and restriction to problems for which an external teacher is available. A promising alternative is to develop unsupervised learning algorithms which can adaptively learn to encode the statistical regularities of the input patterns, without being told explicitly the correct response for each pattern. In this paper, we describe the major approaches that have been taken to model unsupervised learning, and give an in-depth review of several examples of each approach. </jats:p>
収録刊行物
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- International Journal of Neural Systems
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International Journal of Neural Systems 02 (01n02), 17-33, 1991-01
World Scientific Pub Co Pte Lt
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詳細情報
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- CRID
- 1362544418894411648
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- NII論文ID
- 30009025342
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- NII書誌ID
- AA10724079
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- ISSN
- 17936462
- 01290657
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- データソース種別
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