A state of the art specialist monograph on artificial neural networks which use Hebbian learning, covering a wide range of real experiments and which displays how it’s approaches can be applied to analyse real problems. The book has a thorough approach and brings together a wide range of concepts into a coherent whole. Colin Fyfe writes with authority, and is a well-known, experienced researcher who has led a team working in this area at Paisley.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
From the reviews of the first edition:
"This book is concerned with developing unsupervised learning procedures and building self organizing network modules that can capture regularities of the environment. ... the book provides a detailed introduction to Hebbian learning and negative feedback neural networks and is suitable for self-study or instruction in an introductory course." (Nicolae S. Mera, Zentralblatt MATH, Vol. 1069, 2005)
Introduction Part I - Single Stream Networks Background The Negative Feedback Network Peer-Inhibitory Neurons Multiple Cause Data Exploratory Data Analysis Topology Preserving Maps Maximum Likelihood Hebbian Learning Part II - Dual Stream Networks Two Neural Networks for Canonical Correlation Analysis Alternative Derivations of CCA Networks Kernel and Nonlinear Correlations Exploratory Correlation Analysis Multicollinearity and Partial Least Squares Twinned Principal curves The Future App. A. Negative Feedback Artificial Neural Networks B. Previous Factor Analysis Models C. Related Models for ICA D. Previous Dual Stream Approaches E. Data Sets References Index
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Destinazione, tempi e costiDa: moluna, Greven, Germania
Kartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Concentrates on one specific architecture and learning rule which no other book doesState of the art in artificial neural networks which use Hebbian learningA comparative study of a variety of techniques that have been drawn from extensions. Codice articolo 4289001
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is the outcome of a decade's research into a speci c architecture and associated learning mechanism for an arti cial neural network: the - chitecture involves negative feedback and the learning mechanism is simple Hebbian learning. The research began with my own thesis at the University of Strathclyde, Scotland, under Professor Douglas McGregor which culminated with me being awarded a PhD in 1995 [52], the title of which was 'Negative Feedback as an Organising Principle for Arti cial Neural Networks'. Naturally enough, having established this theme, when I began to sup- vise PhD students of my own, we continued to develop this concept and this book owes much to the research and theses of these students at the Applied Computational Intelligence Research Unit in the University of Paisley. Thus we discuss work from - Dr. Darryl Charles [24] in Chapter 5. - Dr. Stephen McGlinchey [127] in Chapter 7. - Dr. Donald MacDonald [121] in Chapters 6 and 8. - Dr. Emilio Corchado [29] in Chapter 8. We brie y discuss one simulation from the thesis of Dr. Mark Girolami [58] in Chapter 6 but do not discuss any of the rest of his thesis since it has already appeared in book form [59]. We also must credit Cesar Garcia Osorio, a current PhD student, for the comparative study of the two Exploratory Projection Pursuit networks in Chapter 8. All of Chapters 3 to 8 deal with single stream arti cial neural networks. 404 pp. Englisch. Codice articolo 9781849969451
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Da: AHA-BUCH GmbH, Einbeck, Germania
Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is the outcome of a decade's research into a speci c architecture and associated learning mechanism for an arti cial neural network: the - chitecture involves negative feedback and the learning mechanism is simple Hebbian learning. The research began with my own thesis at the University of Strathclyde, Scotland, under Professor Douglas McGregor which culminated with me being awarded a PhD in 1995 [52], the title of which was 'Negative Feedback as an Organising Principle for Arti cial Neural Networks'. Naturally enough, having established this theme, when I began to sup- vise PhD students of my own, we continued to develop this concept and this book owes much to the research and theses of these students at the Applied Computational Intelligence Research Unit in the University of Paisley. Thus we discuss work from - Dr. Darryl Charles [24] in Chapter 5. - Dr. Stephen McGlinchey [127] in Chapter 7. - Dr. Donald MacDonald [121] in Chapters 6 and 8. - Dr. Emilio Corchado [29] in Chapter 8. We brie y discuss one simulation from the thesis of Dr. Mark Girolami [58] in Chapter 6 but do not discuss any of the rest of his thesis since it has already appeared in book form [59]. We also must credit Cesar Garcia Osorio, a current PhD student, for the comparative study of the two Exploratory Projection Pursuit networks in Chapter 8. All of Chapters 3 to 8 deal with single stream arti cial neural networks. Codice articolo 9781849969451
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Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. pp. xviii + 383 1st Edition. Codice articolo 262142939
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Da: Majestic Books, Hounslow, Regno Unito
Condizione: New. Print on Demand pp. xviii + 383. Codice articolo 5704964
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Da: Biblios, Frankfurt am main, HESSE, Germania
Condizione: New. PRINT ON DEMAND pp. xviii + 383. Codice articolo 182142929
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Da: Mispah books, Redhill, SURRE, Regno Unito
Paperback. Condizione: Like New. Like New. book. Codice articolo ERICA79618499694506
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Da: Revaluation Books, Exeter, Regno Unito
Paperback. Condizione: Brand New. 401 pages. 9.13x6.06x1.02 inches. In Stock. Codice articolo zk1849969450
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