Soft cover. Condizione: Near fine. Founded in 1989 by Terrence Sejnowski, Neural Computation became the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years. This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. It is of interest to engineers working in areas of computer vision and speech recognition who seek efficient representations of raw input data. Top edge of the front cover with a tiny ding. 6" - 9". book.
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paperback. Condizione: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority!
Lingua: Inglese
Editore: MIT Press, Cambridge, Massachussetts, 1999
ISBN 10: 026258168X ISBN 13: 9780262581684
Da: Ethan Daniel Books, Toronto, ON, Canada
Prima edizione
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Aggiungi al carrelloLight spotting to fore-edge of text block, but not to inside pages. Otherwise a clean copy with no ownership marks or underlining and no reading crease to spine. 398 pages. s254.
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Paperback or Softback. Condizione: New. Unsupervised Learning: Foundations of Neural Computation. Book.
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paperback. Condizione: New. In shrink wrap. Looks like an interesting title!
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Aggiungi al carrelloCondizione: New. This volume on neural network learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. Editor(s): Hinton, Geoffrey E.; Sejnowski, Terrence J. Series: Computational Neuroscience. Num Pages: 414 pages, Illustrations. BIC Classification: PSAN; UGK; UYA; UYQN. Category: (P) Professional & Vocational. Dimension: 229 x 152 x 22. Weight in Grams: 544. . 1999. Illustrated. paperback. . . . . Books ship from the US and Ireland.
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Aggiungi al carrelloCondizione: New. This volume on neural network learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. Editor(s): Hinton, Geoffrey E.; Sejnowski, Terrence J. Series: Computational Neuroscience. Num Pages: 414 pages, Illustrations. BIC Classification: PSAN; UGK; UYA; UYQN. Category: (P) Professional & Vocational. Dimension: 229 x 152 x 22. Weight in Grams: 544. . 1999. Illustrated. paperback. . . . .
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Aggiungi al carrelloPaperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Da: moluna, Greven, Germania
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Geoffrey Hinton is Professor of Computer Science at the University of Toronto.Terrence J. Sejnowski holds the Francis Crick Chair at the Salk Institute for Biological Studies and is a Distinguished Professor at the University of California, San Dieg.
Da: preigu, Osnabrück, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Unsupervised Learning | Foundations of Neural Computation | Geoffrey Hinton (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 1999 | MIT Press | EAN 9780262581684 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 60,14
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This volume, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.