Edited volume written by leading experts providing state-of-art survey in on-line learning and neural networks.
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Anbieter: AwesomeBooks, Wallingford, Vereinigtes Königreich
hardcover. Zustand: Very Good. On-Line Learning in Neural Networks: 17 (Publications of the Newton Institute, Series Number 17) This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. . Artikel-Nr. 7719-9780521652636
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Anbieter: Bahamut Media, Reading, Vereinigtes Königreich
hardcover. Zustand: Very Good. Shipped within 24 hours from our UK warehouse. Clean, undamaged book with no damage to pages and minimal wear to the cover. Spine still tight, in very good condition. Remember if you are not happy, you are covered by our 100% money back guarantee. Artikel-Nr. 6545-9780521652636
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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. Artikel-Nr. 149295
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Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
Zustand: New. pp. 412 40 Illus. Artikel-Nr. 8309379
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Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
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Anbieter: Kennys Bookstore, Olney, MD, USA
Zustand: New. Edited volume written by leading experts providing state-of-art survey in on-line learning and neural networks. Editor(s): Saad, David. Series: Publications of the Newton Institute. Num Pages: 412 pages, 40 b/w illus. BIC Classification: UYQN. Category: (P) Professional & Vocational. Dimension: 235 x 159 x 31. Weight in Grams: 2362. . 1999. Illustrated. hardcover. . . . . Books ship from the US and Ireland. Artikel-Nr. V9780521652636
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Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Artikel-Nr. 9780521652636
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