Information Theory and Statistical Learning
Information Theory and Statistical Learning presents theoretical and practical results about information theoretic methods used in the context of statistical learning. The book will present a comprehensive overview of the large range of different methods that have been developed in a multitude of co...
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
Boston, MA :
Springer US,
2009.
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| Online Access: | Full Text via HEAL-Link |
Table of Contents:
- Algorithmic Probability: Theory and Applications
- Model Selection and Testing by the MDL Principle
- Normalized Information Distance
- The Application of Data Compression-Based Distances to Biological Sequences
- MIC: Mutual Information Based Hierarchical Clustering
- A Hybrid Genetic Algorithm for Feature Selection Based on Mutual Information
- Information Approach to Blind Source Separation and Deconvolution
- Causality in Time Series: Its Detection and Quantification by Means of Information Theory
- Information Theoretic Learning and Kernel Methods
- Information-Theoretic Causal Power
- Information Flows in Complex Networks
- Models of Information Processing in the Sensorimotor Loop
- Information Divergence Geometry and the Application to Statistical Machine Learning
- Model Selection and Information Criterion
- Extreme Physical Information as a Principle of Universal Stability
- Entropy and Cloning Methods for Combinatorial Optimization, Sampling and Counting Using the Gibbs Sampler.
