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Publicado por Springer, 2012
ISBN 10: 9401061041ISBN 13: 9789401061049
Librería: booksXpress, Bayonne, NJ, Estados Unidos de America
Libro
Soft Cover. Condición: new.
Publicado por Springer Netherlands, 2012
ISBN 10: 9401061041ISBN 13: 9789401061049
Librería: moluna, Greven, Alemania
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Proceedings of the NATO Advanced Study Institute, Ettore Maiorana Centre, Erice, Italy, September 27-October 7, 1996 In the past decade, a number of different research communities within the computational sciences have studied learning in networks, s.
Publicado por Springer, 2012
ISBN 10: 9401061041ISBN 13: 9789401061049
Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Condición: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book.
Publicado por Springer, 2012
ISBN 10: 9401061041ISBN 13: 9789401061049
Librería: Lucky's Textbooks, Dallas, TX, Estados Unidos de America
Libro
Condición: New.
Publicado por Springer Netherlands Okt 2012, 2012
ISBN 10: 9401061041ISBN 13: 9789401061049
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In the past decade, a number of different research communities within the computational sciences have studied learning in networks, starting from a number of different points of view. There has been substantial progress in these different communities and surprising convergence has developed between the formalisms. The awareness of this convergence and the growing interest of researchers in understanding the essential unity of the subject underlies the current volume. Two research communities which have used graphical or network formalisms to particular advantage are the belief network community and the neural network community. Belief networks arose within computer science and statistics and were developed with an emphasis on prior knowledge and exact probabilistic calculations. Neural networks arose within electrical engineering, physics and neuroscience and have emphasised pattern recognition and systems modelling problems. This volume draws together researchers from these two communities and presents both kinds of networks as instances of a general unified graphical formalism. The book focuses on probabilistic methods for learning and inference in graphical models, algorithm analysis and design, theory and applications. Exact methods, sampling methods and variational methods are discussed in detail. Audience: A wide cross-section of computationally oriented researchers, including computer scientists, statisticians, electrical engineers, physicists and neuroscientists. 648 pp. Englisch.
Publicado por Springer Netherlands, 2012
ISBN 10: 9401061041ISBN 13: 9789401061049
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Libro
Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - In the past decade, a number of different research communities within the computational sciences have studied learning in networks, starting from a number of different points of view. There has been substantial progress in these different communities and surprising convergence has developed between the formalisms. The awareness of this convergence and the growing interest of researchers in understanding the essential unity of the subject underlies the current volume. Two research communities which have used graphical or network formalisms to particular advantage are the belief network community and the neural network community. Belief networks arose within computer science and statistics and were developed with an emphasis on prior knowledge and exact probabilistic calculations. Neural networks arose within electrical engineering, physics and neuroscience and have emphasised pattern recognition and systems modelling problems. This volume draws together researchers from these two communities and presents both kinds of networks as instances of a general unified graphical formalism. The book focuses on probabilistic methods for learning and inference in graphical models, algorithm analysis and design, theory and applications. Exact methods, sampling methods and variational methods are discussed in detail. Audience: A wide cross-section of computationally oriented researchers, including computer scientists, statisticians, electrical engineers, physicists and neuroscientists.
Publicado por Springer, 2012
ISBN 10: 9401061041ISBN 13: 9789401061049
Librería: Books Puddle, New York, NY, Estados Unidos de America
Libro
Condición: New. pp. 648.
Publicado por Springer, 2012
ISBN 10: 9401061041ISBN 13: 9789401061049
Librería: Majestic Books, Hounslow, Reino Unido
Libro Impresión bajo demanda
Condición: New. Print on Demand pp. 648 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.