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Advances in Independent Component Analysis

Overview of attention for book
Cover of 'Advances in Independent Component Analysis'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    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 2: Particle Filters for Non-Stationary ICA
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Chapter title
Particle Filters for Non-Stationary ICA
Chapter number 2
Book title
Advances in Independent Component Analysis
Published by
Springer, London, January 2000
DOI 10.1007/978-1-4471-0443-8_2
Book ISBNs
978-1-85233-263-1, 978-1-4471-0443-8
Authors

Richard M. Everson, Stephen J. Roberts, Everson, Richard M., Roberts, Stephen J.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 1 100%
Readers by discipline Count As %
Engineering 1 100%

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