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Artificial Neural Networks - ICANN 2010: 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part I - Couverture souple
Synopsis
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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Artificial Neural Networks - Icann 2010 : 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part I
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Artificial Neural Networks - ICANN 2010: 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part I (Lecture Notes in Computer Science, 6352)
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Artificial Neural Networks - ICANN 2010
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Springer Berlin Heidelberg Sep 2010, 2010
ISBN 10 : 3642158188
ISBN 13 : 9783642158186
Neuf
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - 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 signalsmaybesenttootherunitsalongconnectionsknownasweightsthat excite 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. 620 pp. Englisch. N° de réf. du vendeur 9783642158186
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Artificial Neural Networks - Icann 2010 : 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part I
Edité par
Springer, 2010
ISBN 10 : 3642158188
ISBN 13 : 9783642158186
Ancien ou d'occasion
Couverture souple
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Artificial Neural Networks - ICANN 2010
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ISBN 10 : 3642158188
ISBN 13 : 9783642158186
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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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- saloniki, Greece during Se. N° de réf. du vendeur 5050949
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Artificial Neural Networks - Icann 2010 : 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part I
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Artificial Neural Networks - Icann 2010 : 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part I
Edité par
Springer, 2010
ISBN 10 : 3642158188
ISBN 13 : 9783642158186
Ancien ou d'occasion
Couverture souple
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Artificial Neural Networks - ICANN 2010
Edité par
Springer, Springer Sep 2010, 2010
ISBN 10 : 3642158188
ISBN 13 : 9783642158186
Neuf
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. 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 signalsmaybesenttootherunitsalongconnectionsknownasweightsthatexci te 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.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 620 pp. Englisch. N° de réf. du vendeur 9783642158186
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Artificial Neural Networks - ICANN 2010 : 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part I
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Springer Spektrum, Springer, 2010
ISBN 10 : 3642158188
ISBN 13 : 9783642158186
Neuf
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783642158186
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Artificial Neural Networks - ICANN 2010 : 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part I
Edité par
Springer, 2010
ISBN 10 : 3642158188
ISBN 13 : 9783642158186
Ancien ou d'occasion
Couverture souple
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Etat : Sehr gut. Zustand: Sehr gut | Seiten: 620 | 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. N° de réf. du vendeur 8751754/12
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