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Publicado por Kluwer Academic Publishers, Boston, MA, 1998
ISBN 10: 0792382900 ISBN 13: 9780792382904
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Idioma: Inglés
Publicado por Kluwer Academic Publishers, 1998
ISBN 10: 0792382900 ISBN 13: 9780792382904
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Añadir al carritoCondición: New. This text presents a unified methodology for designing modular neural networks. A family of online algorithms for time series classification, prediction and identification are developed; and a rigorous mathematical analysis of their properties is provided. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 325 pages, biography. BIC Classification: UYQN. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 19. Weight in Grams: 642. . 1998. Hardback. . . . .
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Idioma: Inglés
Publicado por Kluwer Academic Publishers, 1998
ISBN 10: 0792382900 ISBN 13: 9780792382904
Librería: Kennys Bookstore, Olney, MD, Estados Unidos de America
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Añadir al carritoCondición: New. This text presents a unified methodology for designing modular neural networks. A family of online algorithms for time series classification, prediction and identification are developed; and a rigorous mathematical analysis of their properties is provided. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 325 pages, biography. BIC Classification: UYQN. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 19. Weight in Grams: 642. . 1998. Hardback. . . . . Books ship from the US and Ireland.
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The subject of this book is predictive modular neural networks and their ap plication to time series problems: classification, prediction and identification. The intended audience is researchers and graduate students in the fields of neural networks, computer science, statistical pattern recognition, statistics, control theory and econometrics. Biologists, neurophysiologists and medical engineers may also find this book interesting. In the last decade the neural networks community has shown intense interest in both modular methods and time series problems. Similar interest has been expressed for many years in other fields as well, most notably in statistics, control theory, econometrics etc. There is a considerable overlap (not always recognized) of ideas and methods between these fields. Modular neural networks come by many other names, for instance multiple models, local models and mixtures of experts. The basic idea is to independently develop several 'subnetworks' (modules), which may perform the same or re lated tasks, and then use an 'appropriate' method for combining the outputs of the subnetworks. Some of the expected advantages of this approach (when compared with the use of 'lumped' or 'monolithic' networks) are: superior performance, reduced development time and greater flexibility. For instance, if a module is removed from the network and replaced by a new module (which may perform the same task more efficiently), it should not be necessary to retrain the aggregate network. 332 pp. Englisch.
Idioma: Inglés
Publicado por Springer US, Springer New York Okt 2012, 2012
ISBN 10: 1461375401 ISBN 13: 9781461375401
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The subject of this book is predictive modular neural networks and their ap plication to time series problems: classification, prediction and identification. The intended audience is researchers and graduate students in the fields of neural networks, computer science, statistical pattern recognition, statistics, control theory and econometrics. Biologists, neurophysiologists and medical engineers may also find this book interesting. In the last decade the neural networks community has shown intense interest in both modular methods and time series problems. Similar interest has been expressed for many years in other fields as well, most notably in statistics, control theory, econometrics etc. There is a considerable overlap (not always recognized) of ideas and methods between these fields. Modular neural networks come by many other names, for instance multiple models, local models and mixtures of experts. The basic idea is to independently develop several 'subnetworks' (modules), which may perform the same or re lated tasks, and then use an 'appropriate' method for combining the outputs of the subnetworks. Some of the expected advantages of this approach (when compared with the use of 'lumped' or 'monolithic' networks) are: superior performance, reduced development time and greater flexibility. For instance, if a module is removed from the network and replaced by a new module (which may perform the same task more efficiently), it should not be necessary to retrain the aggregate network.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 332 pp. Englisch.