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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 , 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  in Chapter 5. • Dr. Stephen McGlinchey  in Chapter 7. • Dr. Donald MacDonald  in Chapters 6 and 8. • Dr. Emilio Corchado  in Chapter 8. We brie?y discuss one simulation from the thesis of Dr. Mark Girolami  in Chapter 6 but do not discuss any of the rest of his thesis since it has already appeared in book form . 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.
Part I - Single Stream Networks
The Negative Feedback Network
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
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