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Information Theory and Statistical Learning

Overview of attention for book
Cover of 'Information Theory and Statistical Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Algorithmic Probability: Theory and Applications
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    Chapter 2 Model Selection and Testing by the MDL Principle
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    Chapter 3 Normalized Information Distance
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    Chapter 4 The Application of Data Compression-Based Distances to Biological Sequences
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    Chapter 5 MIC: Mutual Information Based Hierarchical Clustering
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    Chapter 6 A Hybrid Genetic Algorithm for Feature Selection Based on Mutual Information
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    Chapter 7 Information Approach to Blind Source Separation and Deconvolution
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    Chapter 8 Causality in Time Series: Its Detection and Quantification by Means of Information Theory
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    Chapter 9 Information Theoretic Learning and Kernel Methods
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    Chapter 10 Information-Theoretic Causal Power
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    Chapter 11 Information Flows in Complex Networks
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    Chapter 12 Models of Information Processing in the Sensorimotor Loop
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    Chapter 13 Information Divergence Geometry and the Application to Statistical Machine Learning
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    Chapter 14 Model Selection and Information Criterion
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    Chapter 15 Extreme Physical Information as a Principle of Universal Stability
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    Chapter 16 Entropy and Cloning Methods for Combinatorial Optimization, Sampling and Counting Using the Gibbs Sampler
Attention for Chapter 7: Information Approach to Blind Source Separation and Deconvolution
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5 Mendeley
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Chapter title
Information Approach to Blind Source Separation and Deconvolution
Chapter number 7
Book title
Information Theory and Statistical Learning
Published by
Springer, Boston, MA, January 2009
DOI 10.1007/978-0-387-84816-7_7
Book ISBNs
978-0-387-84815-0, 978-0-387-84816-7
Authors

Pham Dinh-Tuan, Dinh-Tuan, Pham

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 20%
Unknown 4 80%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 40%
Professor > Associate Professor 1 20%
Student > Postgraduate 1 20%
Student > Doctoral Student 1 20%
Readers by discipline Count As %
Computer Science 2 40%
Mathematics 1 20%
Agricultural and Biological Sciences 1 20%
Unknown 1 20%

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