9780521652636 - On-Line Learning in Neural Networks (16 risultati)

Hardcover. Condizione: Near fine. Publications of the Newton Institute. x, 398 p. 24 cm. Ink signature.

Condizione: New. pp. 412.

In-8, 408pp., illustr., figures, reliure cart. avec jaquette, A8T,

Condizione: New. pp. 412 40 Illus.

Condizione: New.

Condizione: New. In.

Condizione: 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. . . . .

Condizione: 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.

Buch. Condizione: 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.

Hardcover. Condizione: 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 large layered networks. This book presents a picture of the state-of-the-art in the subject. An introduction relates it to other developments in neural networked and explains the overall picture. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condizione: Brand New. 398 pages. 9.50x6.50x1.25 inches. In Stock. This item is printed on demand.

Hardcover. Condizione: 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 large layered networks. This book presents a picture of the state-of-the-art in the subject. An introduction relates it to other developments in neural networked and explains the overall picture. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. On-line learning is one of the most commonly used techniques for training large layered networks. Traditional methods have been recently complemented by ones from statistical physics and Bayesian statistics to provide more insight and deeper understanding o.

Hardback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Buch. Condizione: Neu. On-Line Learning in Neural Networks | David Saad | Buch | Gebunden | Englisch | 2010 | Cambridge University Press | EAN 9780521652636 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Condizione: New. PRINT ON DEMAND pp. 412.