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Perspectives in Neural Computing : Perspectives in Neural Computing - Mark Girolami

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Perspectives in Neural Computing

By: Mark Girolami (Editor)

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Paperback | 17 July 2000

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304 Pages

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22.86 x 15.24 x 1.91

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Independent Component Analysis (ICA) is a fast developing area of intense research interest. Following on from Self-Organising Neural Networks: Independent Component Analysis and Blind Signal Separation, this book reviews the significant developments of the past year.
It covers topics such as the use of hidden Markov methods, the independence assumption, and topographic ICA, and includes tutorial chapters on Bayesian and variational approaches. It also provides the latest approaches to ICA problems, including an investigation into certain "hard problems" for the very first time.
Comprising contributions from the most respected and innovative researchers in the field, this volume will be of interest to students and researchers in computer science and electrical engineering; research and development personnel in disciplines such as statistical modelling and data analysis; bio-informatic workers; and physicists and chemists requiring novel data analysis methods.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Computer Networking & Communications Science Biology, Life Sciences Mathematics Probability & Statistics Gardening Mathematical Theory of Computation

Science in General

Contributors
Foreword
Temporal ICA Models
Hidden Markov Independent Component Analysis	p. 3
Particle Filters for Non-Stationary ICA	p. 23
The Validity of the Independence Assumption
The Independence Assumption: Analyzing the Independence of the Components by Topography	p. 45
The Independence Assumption: Dependent Component Analysis	p. 63
Ensemble Learning and Applications
Ensemble Learning	p. 75
Bayesian Non-Linear Independent Component Analysis by Multi-Layer Perceptrons	p. 93
Ensemble Learning for Blind Image Separation and Deconvolution	p. 123
Data Analysis and Application
Multi-Class Independent Component Analysis (MUCICA) for Rank-Deficient Distributions	p. 145
Blind Separation of Noisy Image Mixtures	p. 161
Searching for Independence in Electromagnetic Brain Waves	p. 183
ICA on Noisy Data: A Factor Analysis Approach	p. 201
Analysis of Optical Imaging Data Using Weak Models and ICA	p. 217
Independent Components in Text	p. 235
Seeking Independence Using Biologically-Inspired ANN's	p. 257
Index	p. 277
Table of Contents provided by Blackwell. All Rights Reserved.

Perspectives in Neural Computing

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Paperback

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