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e learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems,
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Tom Hoppenbrouwers,Diego Urbina,Andrea Boyd,Barbara Imhof,Susmita Mohanty,Peter Weiss,Andreas Diekma Models and Applications covers a wealth of important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds
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Wallace Tai,InKyu Kim,SangMan Moon,Day Young Kim,Kar-Ming Cheung,Cheol Hea Koo,James Schier,Dong Youd urgent attention on usable, practical solutions. We discuss a combination and improvement of classical methods to produce robust recognition of hand-printed English text, for a recognizer shipping in new models of Apple Computer’s Newton MessagePad® and eMate®. Combining an artificial neural netwo
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Daniel D. Wenkert,Roy E. Gladden,Charles D. Edwards,Peter Schmitz,Michel Denis,Alistair J. Wintond urgent attention on usable, practical solutions. We discuss a combination and improvement of classical methods to produce robust recognition of hand-printed English text, for a recognizer shipping in new models of Apple Computer’s Newton MessagePad® and eMate®. Combining an artificial neural netwo