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Advances in Independent Component Analysis

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Cover of 'Advances in Independent Component Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 Hidden Markov Independent Component Analysis
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    Chapter 2 Particle Filters for Non-Stationary ICA
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    Chapter 3 The Independence Assumption: Analyzing the Independence of the Components by Topography
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    Chapter 4 The Independence Assumption: Dependent Component Analysis
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    Chapter 5 Ensemble Learning
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    Chapter 6 Bayesian Non-Linear Independent Component Analysis by Multi-Layer Perceptrons
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    Chapter 7 Ensemble Learning for Blind Image Separation and Deconvolution
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    Chapter 8 Multi-Class Independent Component Analysis (MUCICA) for Rank-Deficient Distributions
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    Chapter 9 Blind Separation of Noisy Image Mixtures
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    Chapter 10 Searching for Independence in Electromagnetic Brain Waves
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    Chapter 11 ICA on Noisy Data: A Factor Analysis Approach
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    Chapter 12 Analysis of Optical Imaging Data Using Weak Models and ICA
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    Chapter 13 Independent Components in Text
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    Chapter 14 Seeking Independence Using Biologically-Inspired ANN’s
Attention for Chapter 6: Bayesian Non-Linear Independent Component Analysis by Multi-Layer Perceptrons
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