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Zustand: Very Good. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Zustand: very_good. This book is in Very good condition. There may be a few flaws like shelf wear and some light wear.

Hardcover. Zustand: Very Good. Very Good - Crisp, clean, unread book with some shelfwear/edgewear, may have a remainder mark - NICE Oversized.

Zustand: New.

Paperback or Softback. Zustand: New. Artificial Neural Networks - ICANN 2010: 20th International Conference, Thessaloniki, Greece, Septmeber 15-18, 2010, Proceedings, Part II. Book.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Paperback. Zustand: 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 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. 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.…

Hardcover. Zustand: Fine. 1st Edition. No Markings.

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24 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.

Zustand: As New. Unread book in perfect condition.

Paperback. Zustand: Brand New. 543 pages. 9.00x6.00x1.25 inches. In Stock.

Paperback. Zustand: Brand New. 2010 edition. 596 pages. 9.50x6.00x1.25 inches. In Stock.

Taschenbuch. Zustand: 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.…

hardcover. Zustand: new.

Zustand: 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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PF. Zustand: New.

Zustand: New. pp. 596.

Zustand: New. pp. 596.

Zustand: As New. Unread book in perfect condition.

Zustand: New. In English.

Zustand: New.

Zustand: 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.…

Zustand: As New. Unread book in perfect condition.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 575 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Taschenbuch. Zustand: 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.…

Zustand: new.