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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 2: Model Selection and Testing by the MDL Principle
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20 Mendeley
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Chapter title
Model Selection and Testing by the MDL Principle
Chapter number 2
Book title
Information Theory and Statistical Learning
Published by
Springer, Boston, MA, January 2009
DOI 10.1007/978-0-387-84816-7_2
Book ISBNs
978-0-387-84815-0, 978-0-387-84816-7
Authors

Jorma Rissanen

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 5%
Unknown 19 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 30%
Professor 3 15%
Student > Bachelor 2 10%
Researcher 2 10%
Professor > Associate Professor 2 10%
Other 4 20%
Unknown 1 5%
Readers by discipline Count As %
Computer Science 7 35%
Engineering 4 20%
Mathematics 3 15%
Agricultural and Biological Sciences 2 10%
Psychology 1 5%
Other 1 5%
Unknown 2 10%

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