Idioma: Inglés
Publicado por Wiley & Sons, Incorporated, John, 1996
ISBN 10: 0471054364 ISBN 13: 9780471054368
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Idioma: Inglés
Publicado por John Wiley & Sons 02/n /22 J, 1996
ISBN 10: 0471054364 ISBN 13: 9780471054368
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Idioma: Inglés
Publicado por Palgrave Macmillan, New York, 2009
ISBN 10: 0230610609 ISBN 13: 9780230610606
Librería: Second Story Books, ABAA, Rockville, MD, Estados Unidos de America
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Añadir al carritoHardcover. First Edition, First Printing. Large Octavo, vi, vii, viii, x, 292 pages. In Very Good condition. Bound in the publisher's light blue with green cloth bearing green and white lettering to the spine. Boards have very slight wear. Text block has very slight wear. Illustrated. First edition, first printing. NOTE: Shelved in Locked Annex, Column CC. Oversized book(s). Additional postage necessary for international/expedited orders. Economy International unavailable due to size/weight restrictions. For international/expedited customers, please inquire for rates. 1412102. FP New Rockville Stock.
Idioma: Inglés
Publicado por Palgrave Macmillan, New York, 2009
ISBN 10: 0230610609 ISBN 13: 9780230610606
Librería: Second Story Books, ABAA, Rockville, MD, Estados Unidos de America
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Añadir al carritoHardcover. Octavo, x, 292 pages. In Good plus condition. Spine is blue with black print. Boards in blue illustrated paper. Glue residue on rear from removed label. Illustrated: b&w tables. NOTE: Shelved in Netdesk Column G. 1378675. FP New Rockville Stock.
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Añadir al carritoPaperback or Softback. Condición: New. Artificial Neural Networks - ICANN 2010: 20th International Conference, Thessaloniki, Greece, Septmeber 15-18, 2010, Proceedings, Part II. Book.
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Idioma: Inglés
Publicado por Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2010
ISBN 10: 3642158242 ISBN 13: 9783642158247
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Añadir al carritoPaperback. Condición: new. Paperback. th This volume is part of the three-volume proceedings of the 20 International Conference on Arti?cial Neural Networks (ICANN 2010) that was held in Th- saloniki, Greece during September 1518, 2010. ICANN is an annual meeting sponsored by the European Neural Network Society (ENNS) in cooperation with the International Neural Network So- ety (INNS) and the Japanese Neural Network Society (JNNS). This series of conferences has been held annually since 1991 in Europe, covering the ?eld of neurocomputing, learning systems and other related areas. As in the past 19 events, ICANN 2010 provided a distinguished, lively and interdisciplinary discussion forum for researches and scientists from around the globe. Ito?eredagoodchanceto discussthe latestadvancesofresearchandalso all the developments and applications in the area of Arti?cial Neural Networks (ANNs). ANNs provide an information processing structure inspired by biolo- cal nervous systems and they consist of a large number of highly interconnected processing elements (neurons). Each neuron is a simple processor with a limited computing capacity typically restricted to a rule for combining input signals (utilizing an activation function) in order to calculate the output one. Output signalsmaybesenttootherunitsalongconnectionsknownasweightsth atexcite or inhibit the signal being communicated. ANNs have the ability to learn by example (a large volume of cases) through several iterations without requiring a priori ?xed knowledge of the relationships between process parameters. th This volume is part of the three-volume proceedings of the 20 International Conference on Arti?cial Neural Networks (ICANN 2010) that was held in Th- saloniki, Greece during September 1518, 2010. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Idioma: Inglés
Publicado por Wiley-Interscience, Ny, 1996
ISBN 10: 0471054364 ISBN 13: 9780471054368
Librería: Feldman's Books, Menlo Park, CA, Estados Unidos de America
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Añadir al carrito24 cm. original hardcover. xii,256 pp. diagrams. bibliography. index. "Adaptive and Learning Systems for Signal Processing, Communications, and Control". -(owner's name, otherwise (very) good). 555g.
Idioma: Inglés
Publicado por Springer-Verlag New York Inc, 2010
ISBN 10: 3642158218 ISBN 13: 9783642158216
Librería: Revaluation Books, Exeter, Reino Unido
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Añadir al carritoPaperback. Condición: Brand New. 543 pages. 9.00x6.00x1.25 inches. In Stock.
Idioma: Inglés
Publicado por Springer-Verlag New York Inc, 2010
ISBN 10: 3642158242 ISBN 13: 9783642158247
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Idioma: Inglés
Publicado por Springer Berlin Heidelberg, 2010
ISBN 10: 3642158242 ISBN 13: 9783642158247
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Añadir al carritoCondición: New. Fast track conference proceedingUnique visibilityState-of-the-art researchth This volume is part of the three-volume proceedings of the 20 International Conference on Arti?cial Neural Networks (ICANN 2010) that was held in Th- salon.
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Añadir al carritoPF. Condición: New.
Idioma: Inglés
Publicado por Springer Berlin Heidelberg, 2010
ISBN 10: 3642158218 ISBN 13: 9783642158216
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Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - th This volume is part of the three-volume proceedings of the 20 International Conference on Arti cial Neural Networks (ICANN 2010) that was held in Th- saloniki, Greece during September 15-18, 2010. ICANN is an annual meeting sponsored by the European Neural Network Society (ENNS) in cooperation with the International Neural Network So- ety (INNS) and the Japanese Neural Network Society (JNNS). This series of conferences has been held annually since 1991 in Europe, covering the eld of neurocomputing, learning systems and other related areas. As in the past 19 events, ICANN 2010 provided a distinguished, lively and interdisciplinary discussion forum for researches and scientists from around the globe. Ito eredagoodchanceto discussthe latestadvancesofresearchandalso all the developments and applications in the area of Arti cial Neural Networks (ANNs). ANNs provide an information processing structure inspired by biolo- cal nervous systems and they consist of a large number of highly interconnected processing elements (neurons). Each neuron is a simple processor with a limited computing capacity typically restricted to a rule for combining input signals (utilizing an activation function) in order to calculate the output one. Output signalsmaybesenttootherunitsalongconnectionsknownasweightsthatexcite or inhibit the signal being communicated. ANNs have the ability 'to learn' by example (a large volume of cases) through several iterations without requiring a priori xed knowledge of the relationships between process parameters.
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Añadir al carritoTaschenbuch. Condición: Neu. Artificial Neural Networks - ICANN 2010 | 20th International Conference, Thessaloniki, Greece, Septmeber 15-18, 2020, Proceedings, Part II | Konstantinos Diamantaras (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2010 | Springer | EAN 9783642158216 | Verantwortliche Person für die EU: Lauinger, Sonia, Sonia Lauinger, Lauinger Verlag, Heinrich-Köhler-Platz 8, 76187 Karlsruhe, mail[at]lauinger-verlag[dot]de | Anbieter: preigu.
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Idioma: Inglés
Publicado por Springer, Berlin, Springer, 2010
ISBN 10: 3642158242 ISBN 13: 9783642158247
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware - th This volume is part of the three-volume proceedings of the 20 International Conference on Arti cial Neural Networks (ICANN 2010) that was held in Th- saloniki, Greece during September 15-18, 2010. ICANN is an annual meeting sponsored by the European Neural Network Society (ENNS) in cooperation with the International Neural Network So- ety (INNS) and the Japanese Neural Network Society (JNNS). This series of conferences has been held annually since 1991 in Europe, covering the eld of neurocomputing, learning systems and other related areas. As in the past 19 events, ICANN 2010 provided a distinguished, lively and interdisciplinary discussion forum for researches and scientists from around the globe. Ito eredagoodchanceto discussthe latestadvancesofresearchandalso all the developments and applications in the area of Arti cial Neural Networks (ANNs). ANNs provide an information processing structure inspired by biolo- cal nervous systems and they consist of a large number of highly interconnected processing elements (neurons). Each neuron is a simple processor with a limited computing capacity typically restricted to a rule for combining input signals (utilizing an activation function) in order to calculate the output one. Output signalsmaybesenttootherunitsalongconnectionsknownasweightstha texcite or inhibit the signal being communicated. ANNs have the ability 'to learn' by example (a large volume of cases) through several iterations without requiring a priori xed knowledge of the relationships between process parameters.
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Añadir al carritoCondición: Sehr gut. Zustand: Sehr gut | Seiten: 560 | Sprache: Englisch | Produktart: Bücher | th This volume is part of the three-volume proceedings of the 20 International Conference on Arti?cial Neural Networks (ICANN 2010) that was held in Th- saloniki, Greece during September 15¿18, 2010. ICANN is an annual meeting sponsored by the European Neural Network Society (ENNS) in cooperation with the International Neural Network So- ety (INNS) and the Japanese Neural Network Society (JNNS). This series of conferences has been held annually since 1991 in Europe, covering the ?eld of neurocomputing, learning systems and other related areas. As in the past 19 events, ICANN 2010 provided a distinguished, lively and interdisciplinary discussion forum for researches and scientists from around the globe. Ito?eredagoodchanceto discussthe latestadvancesofresearchandalso all the developments and applications in the area of Arti?cial Neural Networks (ANNs). ANNs provide an information processing structure inspired by biolo- cal nervous systems and they consist of a large number of highly interconnected processing elements (neurons). Each neuron is a simple processor with a limited computing capacity typically restricted to a rule for combining input signals (utilizing an activation function) in order to calculate the output one. Output signalsmaybesenttootherunitsalongconnectionsknownasweightsthatexcite or inhibit the signal being communicated. ANNs have the ability ¿to learn¿ by example (a large volume of cases) through several iterations without requiring a priori ?xed knowledge of the relationships between process parameters.