Neural networks for pattern recognition

著者

書誌事項

Neural networks for pattern recognition

Albert Nigrin

MIT Press, c1993

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注記

"A Bradford book."

Includes bibliographical references (p. [399]-405) and index

内容説明・目次

内容説明

In a simple and accessible way it extends embedding field theory into areas of machine intelligence that have not been clearly dealt with before. Neural Networks for Pattern Recognition takes the pioneering work in artificial neural networks by Stephen Grossberg and his colleagues to a new level. In a simple and accessible way it extends embedding field theory into areas of machine intelligence that have not been clearly dealt with before. Following a tutorial of existing neural networks for pattern classification, Nigrin expands on these networks to present fundamentally new architectures that perform realtime pattern classification of embedded and synonymous patterns and that will aid in tasks such as vision, speech recognition, sensor fusion, and constraint satisfaction. Nigrin presents the new architectures in two stages. First he presents a network called Sonnet 1 that already achieves important properties such as the ability to learn and segment continuously varied input patterns in real time, to process patterns in a context sensitive fashion, and to learn new patterns without degrading existing categories. He then removes simplifications inherent in Sonnet 1 and introduces radically new architectures. These architectures have the power to classify patterns that may have similar meanings but that have different external appearances (synonyms). They also have been designed to represent patterns in a distributed fashion, both in short-term and long-term memory.

目次

  • Highlights of adaptive resonance theory
  • classifying spatial patterns
  • classifying temporal patterns
  • multilayer networks and the use of attention
  • representing synonyms
  • specific architectures that use presynaptic inhibition. Appendices: feedforward circuits for normalization and noise suppression
  • network equations used in the simulations of chapter 3
  • network equations used in the simulations of chapter 4.

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