Abstract
This is an introduction to and interpretation of some techniques which have been developed recently for pattern processing. The first part is concerned with the stochastic modelling approach to pattern recognition, which includes structural and statistical aspects. Various varieties of hidden Markov models, which are the basis of the most successful current automatic speech recognition systems, are viewed as a special case of Markov random fields. The second part is concerned with adaptive networks which “learn” to do jobs such as pattern classification, without necessarily containing explicit models of the data distributions. The main approaches covered are the Boltzmann machine (which is also interpreted as a Markov random field) and a recently invented multi-layer perceptron network.
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Bridle, J.S. (1987). Adaptive Networks and Speech Pattern Processing. In: Devijver, P.A., Kittler, J. (eds) Pattern Recognition Theory and Applications. NATO ASI Series, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-83069-3_18
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DOI: https://doi.org/10.1007/978-3-642-83069-3_18
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