Neural Networks for Pattern Recognition

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ID: 289200
1995
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Abstract
Abstract This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models. It also motivates the use of various forms of error functions, and reviews the principal algorithms for error function minimization. As well as providing a detailed discussion of learning and generalization in neural networks, the book also covers the important topics of data processing, feature extraction, and prior knowledge. The book concludes with an extensive treatment of Bayesian techniques and their applications to neural networks.
Reference Key
openalex_W4388297464 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chris Bishop
Journal Oxford University Press eBooks
Year 1995
DOI
10.1093/oso/9780198538493.001.0001
URL
https://doi.org/10.1093/oso/9780198538493.001.0001
Keywords Keywords not found

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