Edited volume written by leading experts providing state-of-art survey in on-line learning and neural networks.
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Anbieter: Attic Books (ABAC, ILAB), London, ON, Kanada
Hardcover. Zustand: Near fine. Publications of the Newton Institute. x, 398 p. 24 cm. Ink signature. Bestandsnummer des Verkäufers 149295
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Brand new book. Fast ship. Please provide full street address as we are not able to ship to P O box address. Bestandsnummer des Verkäufers SHAK100497
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Anbieter: Books Puddle, New York, NY, USA
Zustand: New. pp. 412. Bestandsnummer des Verkäufers 26619868
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In-8, 408pp., illustr., figures, reliure cart. avec jaquette, A8T, Bestandsnummer des Verkäufers 52196
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Zustand: New. pp. 412 40 Illus. Bestandsnummer des Verkäufers 8309379
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Zustand: New. Bestandsnummer des Verkäufers I-9780521652636
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Anbieter: Grand Eagle Retail, Bensenville, IL, USA
Hardcover. Zustand: new. Hardcover. On-line learning is one of the most powerful and commonly used techniques for training large layered networks, and has been used successfully in many real-world applications. Traditional analytical methods have been recently complemented by ones from statistical physics and Bayesian statistics. This powerful combination of analytical methods provides more insight and deeper understanding of existing algorithms, and leads to novel and principled proposals for their improvement. This book presents a coherent picture of the state-of-the-art in the theoretical analysis of on-line learning. An introduction relates the subject to other developments in neural networks and explains the overall picture. Surveys by leading experts in the field combine new and established material and enable non-experts to learn more about the techniques and methods used. This book, the first in the area, provides a comprehensive view of the subject and will be welcomed by mathematicians, scientists and engineers, whether in industry or academia. On-line learning is one of the most commonly used techniques for training neural networks. Though it has been used successfully in many real-world applications, most training methods are based on heuristic observations. The lack of theoretical support damages the credibility as well as the efficiency of neural networks training, making it hard to choose reliable or optimal methods. This book presents a coherent picture of the state of the art in the theoretical analysis of on-line learning. An introduction relates the subject to other developments in neural networks and explains the overall picture. Surveys by leading experts in the field combine new and established material and enable nonexperts to learn more about the techniques and methods used. This book, the first in the area, provides a comprehensive view of the subject and will be welcomed by mathematicians, scientists and engineers, both in industry and academia. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9780521652636
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
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