Hebbian learning negative feedback (7 Ergebnisse)
Sprache: Englisch
Verlag: Springer, 2005
Serie: Advanced Information and Knowledge Processing, Buch 11 von 66. Buch 11 von 66 - Advanced Information and Knowledge Processing
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Sprache: Englisch
Verlag: Springer, 2005
Serie: Advanced Information and Knowledge Processing, Buch 11 von 66. Buch 11 von 66 - Advanced Information and Knowledge Processing
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Zustand: Used. pp. 404 Illus.
Sprache: Englisch
Verlag: Springer, 2005
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Sprache: Englisch
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Sprache: Englisch
Verlag: Springer, 2010
Serie: Advanced Information and Knowledge Processing, Buch 11 von 66. Buch 11 von 66 - Advanced Information and Knowledge Processing
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Taschenbuch. Zustand: 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 rese…arch 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.
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Sprache: Englisch
Verlag: Springer, Springer, 2005
Serie: Advanced Information and Knowledge Processing, Buch 11 von 66. Buch 11 von 66 - Advanced Information and Knowledge Processing
- Hardcover
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Buch. Zustand: 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 be…gan 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.
Sprache: Englisch
Verlag: Springer, 2004
Serie: Advanced Information and Knowledge Processing, Buch 11 von 66. Buch 11 von 66 - Advanced Information and Knowledge Processing
- Softcover
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Paperback. Zustand: Brand New. 401 pages. 9.13x6.06x1.02 inches. In Stock.
