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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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Emmert-Streib, Frank. (Editor), Dehmer, Matthias. (Editor)
Format: Electronic
Language:English
Published: Boston, MA : Springer US, 2009.
Subjects:
Online Access:https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-0-387-84816-7
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 (EPI) as a PRinciple of Universal Stability
  • Entropy and Cloning Methods for Combinatorial Optimization, Sampling and Counting Using the Gibbs Sampler.