Abstract
Feature extraction, classification, clustering and learning in pattern recognition are closely related to the maximum or minimum entropy principles. Such relationships are reviewed in this paper. The need for adaptive pattern recognition using neural networks is then emphasized. A comparison between neural networks and conventional statistical classifiers is also presented.
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© 1990 Kluwer Academic Publishers
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Chen, C.H. (1990). Maximum Entropy Analysis for Pattern Recognition. In: Fougère, P.F. (eds) Maximum Entropy and Bayesian Methods. Fundamental Theories of Physics, vol 39. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0683-9_27
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DOI: https://doi.org/10.1007/978-94-009-0683-9_27
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-6792-8
Online ISBN: 978-94-009-0683-9
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