UNSUPERVISED LEARNING PROCEDURES FOR NEURAL NETWORKS

  • Suzanna Becker
    Department of Computer Science, University of Toronto Toronto, Ontario, M5S 1A4, Canada

この論文をさがす

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

被引用文献 (5)*注記

もっと見る

詳細情報

問題の指摘

ページトップへ