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Information Theory and Statistical Learning

Sofort lieferbar | Lieferzeit: Sofort lieferbar I
ISBN-13:
9780387848167
Seiten:
439
eBook Typ:
PDF
eBook Format:
EPUB
Kopierschutz:
1 - PDF Watermark
Sprache:
Englisch
Beschreibung:

""Information Theory and Statistical Learning" presents theoretical and practical results about information theoretic methods used in the context of statistical learning.
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.

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