{"product_id":"on-line-learning-in-neural-networks-paperback-9780521117913","title":"On-Line Learning in Neural Networks (Paperback) 9780521117913","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eOn-Line Learning in Neural Networks\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eEdited volume written by leading experts providing state-of-art survey in on-line learning and neural networks.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eDavid Saad (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780521117913, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback, published 30 July 2009\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e412 pages, 40 b\/w illus.\u003cbr\u003e22.9 x 15.2 x 2.3 cm, 0.6 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003eReview of the hardback: 'I recommend this book to readers with a theoretical, analytical, or mathematical interest in neural networks, especially online learning.' Computing Reviews\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eOn-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.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eForeword C. Bishop\u003cbr\u003e 1. Introduction D. Saad\u003cbr\u003e 2. On-line learning and stochastic approximations Léon Bottou\u003cbr\u003e 3. Exact and perturbative solutions for the ensemble dynamics Todd Leen\u003cbr\u003e 4. A statistical study of on-line learning Noboru Murata\u003cbr\u003e 5. On-line learning in switching and drifting environments Klaus-Robert Mueller, Andreas Ziehe, Noboru Murata and Shun-ichi Amari\u003cbr\u003e 6. Parameter adaptation in stochastic optimization Luis B. Almeida, Thibault Langlois, José D. Amaral and Alexander Plakhov\u003cbr\u003e 7. Optimal on-line learning for multilayer neural networks David Saad and Magnus Rattray\u003cbr\u003e 8. Universal asymptotics in committee machines with tree architecture Mauro Copelli and Nestor Caticha\u003cbr\u003e 9. Incorporating curvature information in on-line learning Magnus Rattray and David Saad\u003cbr\u003e 10. Annealed on-line learning in multilayer networks Siegfried Bös and Shun-ichi Amari\u003cbr\u003e 11. On-line learning of prototypes and principal components Michael Biehl, Ansgar Freking, Matthias Hölzer, Georg Reents and Enno Schlösser\u003cbr\u003e 12. On-line learning with time-correlated patterns Tom Heskes and Wim Wiegerinck\u003cbr\u003e 13. On-line learning from finite training sets David Barber and Peter Sollich\u003cbr\u003e 14. Dynamics of supervised learning with restricted training sets Anthony C. C. Coolen and David Saad\u003cbr\u003e 15. On-line learning of a decision boundary with and without queries Yoshiyuki Kabashima and Shigeru Shinomoto\u003cbr\u003e 16. A Bayesian approach to on-line learning Manfred Opper\u003cbr\u003e 17. Optimal perception learning: an on-line Bayesian approach Sara A. Solla and Ole Winther.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Neural networks \u0026amp; fuzzy systems [\u003ca title=\"See our other books on Neural networks \u0026amp; fuzzy systems\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Neural%20networks%20\u0026amp;%20fuzzy%20systems%20%5BUYQN%5D%22\"\u003eUYQN\u003c\/a\u003e], Machine learning [\u003ca title=\"See our other books on Machine learning\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Machine%20learning%20%5BUYQM%5D%22\"\u003eUYQM\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":46006528049432,"sku":"9780521117913","price":40.65,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9780521117913i_28d6f3ec-8067-466e-9f4c-c2fd151a1427.jpg?v=1691371582","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/on-line-learning-in-neural-networks-paperback-9780521117913","provider":"Freshly Printed Books","version":"1.0","type":"link"}