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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 15: Extreme Physical Information as a Principle of Universal Stability
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