Hebbian Learning Negative Feedback by Fyfe Colin (27 results)

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Hardcover. Condition: new. Hardcover. The central idea of Hebbian Learning and Negative Feedback Networks is that artificial neural networks using negative feedback of activation can use simple Hebbian learning to self-organise so that they uncover interesting structures in data sets. Two variants are considered: the first uses a single stream of data to self-organise. By changing the learning rules for the network, it is shown how to perform Principal Component Analysis, Exploratory Projection Pursuit, Independent Component Analysis, Factor Analysis and a variety of topology preserving mappings for such data sets. The second variants use two input data streams on which they self-organise. In their basic form, these networks are shown to perform Canonical Correlation Analysis, the statistical technique which finds those filters onto which projections of the two data streams have greatest correlation. The book encompasses a wide range of real experiments and displays how the approaches it formulates can be applied to the analysis of real problems. This book is the outcome of a decades 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Buch. Condition: Neu. 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 404 pp. Englisch.

Taschenbuch. Condition: 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.

Buch. Condition: 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.

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Hardcover. Condition: new. Hardcover. The central idea of Hebbian Learning and Negative Feedback Networks is that artificial neural networks using negative feedback of activation can use simple Hebbian learning to self-organise so that they uncover interesting structures in data sets. Two variants are considered: the first uses a single stream of data to self-organise. By changing the learning rules for the network, it is shown how to perform Principal Component Analysis, Exploratory Projection Pursuit, Independent Component Analysis, Factor Analysis and a variety of topology preserving mappings for such data sets. The second variants use two input data streams on which they self-organise. In their basic form, these networks are shown to perform Canonical Correlation Analysis, the statistical technique which finds those filters onto which projections of the two data streams have greatest correlation. The book encompasses a wide range of real experiments and displays how the approaches it formulates can be applied to the analysis of real problems. This book is the outcome of a decades 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Condition: 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.

Taschenbuch. Condition: 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.

Condition: 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.

Gebunden. Condition: 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.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 404 pp. Englisch.

Condition: New. Print on Demand pp. xviii + 383.

Condition: New. PRINT ON DEMAND pp. xviii + 383.