Self-Organising Neural Networks

1. Front Matter

   Pages i-ix

   PDF

2. Introduction

   • Mark Girolami

   Pages 1-4

3. Background to Blind Source Separation

   • Mark Girolami

   Pages 5-34

4. Fourth Order Cumulant Based Blind Source Separation

   • Mark Girolami

   Pages 35-45

5. Self-Organising Neural Networks

   • Mark Girolami

   Pages 47-75

6. The Non-Linear PCA Algorithm and Blind Source Separation

   • Mark Girolami

   Pages 77-118

7. Non-Linear Feature Extraction and Blind Source Separation

   • Mark Girolami

   Pages 119-163

8. Information Theoretic Non-Linear Feature Extraction and Blind Source Separation

   • Mark Girolami

   Pages 165-200

9. Temporal Anti-Hebbian Learning

   • Mark Girolami

   Pages 201-237

10. Applications

    • Mark Girolami

    Pages 239-254

11. Back Matter

    Pages 255-271

    PDF

The conception of fresh ideas and the development of new techniques for Blind Source Separation and Independent Component Analysis have been rapid in recent years. It is also encouraging, from the perspective of the many scientists involved in this fascinating area of research, to witness the growing list of successful applications of these methods to a diverse range of practical everyday problems. This growth has been due, in part, to the number of promising young and enthusiastic researchers who have committed their efforts to expanding the current body of knowledge within this field of research. The author of this book is among one of their number. I trust that the present book by Dr. Mark Girolami will provide a rapid and effective means of communicating some of these new ideas to a wide international audience and that in turn this will expand further the growth of knowledge. In my opinion this book makes an important contribution to the theory of Independent Component Analysis and Blind Source Separation. This opens a range of exciting methods, techniques and algorithms for applied researchers and practitioner engineers, especially from the perspective of artificial neural networks and information theory. It has been interesting to see how rapidly the scientific literature in this area has grown.

• algorithms
• artificial intelligence
• classification
• data mining
• learning
• neural networks
• optimization
• simulation
• statistics

• Department of Computing and Information Systems, University of Paisley, Paisley, UK

  Mark Girolami

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