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Añadir al carritoPaperback. Condición: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Artificial 'neural networks' are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the 'overfitting' that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.
Librería: moluna, Greven, Alemania
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Artificial neural networks are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesi.
Idioma: Inglés
Publicado por Springer New York Aug 1996, 1996
ISBN 10: 0387947248 ISBN 13: 9780387947242
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 181,89
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Artificial 'neural networks' are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the 'overfitting' that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence. 204 pp. Englisch.
Idioma: Inglés
Publicado por Springer, Springer Aug 1996, 1996
ISBN 10: 0387947248 ISBN 13: 9780387947242
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 -Bayesian methods for neural networks are of interest to researchers in statistics, engineering, and artificial intelligence. This is a very active research area in statistics and artificial intelligence.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 204 pp. Englisch.
Idioma: Inglés
Publicado por Springer-Verlag New York Inc., 1996
ISBN 10: 0387947248 ISBN 13: 9780387947242
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
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Añadir al carritoPaperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Añadir al carritoCondición: New. PRINT ON DEMAND pp. 204.