This text is written by researchers at BT Laboratories and at universities who have investigated a set of problems in vision, speech and natural language. It presents a diversity of approaches to the solution of these problems, based on a range of neural network paradigms. The book also includes sections on implementation, which discuss some approaches to hardware realization; and architectures, which describe new ideas and algorithms. The findings discussed here should be of interest not only to those working in the applications areas discussed, but also to people working more widely in the field of neural networks. This book should be of interest to working researchers and graduate students in neural networks.
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`... admirably clearly written, and well structured throughout...The book succeeds very well in its aims: it does improve on the weaknesses of comparisons between approaches in the literature; it implicitly sets out how industrially oriented research is best carried out; and it presents some interesting, sometimes fascinating new results and approaches.'
Network 3
`This volume collects the more successful case studies from a very large research project at BT Laboratories that set out to investigate the applicability of neural computing to speech, vision and the natural language problems in this application context...of interest to researchers with specialist knowledge of these subjects or, especially, to those with an interest in the application of multi-layer perceptions to practical problems.'
Computing
Introduction; Vision; Neural Networks for Vision: an introduction; Image feature location in multiresolution images using a hierarchy of multilayer perceptrons; Training multilayer perceptrons for facial feature location: a case study; The detection of eyes in facial images using radial basis functions; Training and testing of neural net window operators on spatiotemporal images; A neural network feature detector using a multi-resolution pyramid; Image classification using Gabor representation.Speech; Neural Networks for speech processing: an introduction; Spoken alphabet recognition using multilayer perceptrons; Speaker independent vowel recognition; Dissection of perceptron structures in speech and speaker recognition; Segmental subword unit classification using a multilayer perceptron; Natural language; Connectionist natural language processing: an introduction; A single layer higher-order nueral net and its application to context-free grammer recognition; Functional compositionality and soft preference rules; Application of multilayer perceptrons in text-to-speech synthesis systems. Implementation; Hardware imlementation of neural networks: an introduction; Finite word length MLPs; A VLSI architecture for implementing neural networks with on chip back-propagation learning; An opto-electronic neural network processor; Architecture ; Architecture: an introduction; A dynamic topology net; The stochastic search network; Node sequence networks; Some dynamical properties of neural networks. Index.
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