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Information Theory and Statistical Learning

Erschienen am 04.11.2010, Auflage: 1/2009
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ISBN/EAN: 9781441946508
Sprache: Englisch
Umfang: x, 439 S.
Einband: kartoniertes Buch

Beschreibung

InhaltsangabeAlgorithmic 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.

Autorenportrait

InhaltsangabeAlgorithmic 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.