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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Emmert-Streib, Frank (Επιμελητής έκδοσης), Dehmer, Matthias (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Boston, MA : Springer US, 2009.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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245 1 0 |a Information Theory and Statistical Learning  |h [electronic resource] /  |c edited by Frank Emmert-Streib, Matthias Dehmer. 
264 1 |a Boston, MA :  |b Springer US,  |c 2009. 
300 |a X, 439 p.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
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505 0 |a 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. 
520 |a 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 contexts. Each chapter is written by an expert in the field. The book is intended for an interdisciplinary readership working in machine learning, applied statistics, artificial intelligence, biostatistics, computational biology, bioinformatics, web mining or related disciplines. Advance Praise for Information Theory and Statistical Learning: "A new epoch has arrived for information sciences to integrate various disciplines such as information theory, machine learning, statistical inference, data mining, model selection etc. I am enthusiastic about recommending the present book to researchers and students, because it summarizes most of these new emerging subjects and methods, which are otherwise scattered in many places." -- Shun-ichi Amari, RIKEN Brain Science Institute, Professor-Emeritus at the University of Tokyo. 
650 0 |a Computer science. 
650 0 |a Coding theory. 
650 0 |a Computers. 
650 0 |a Computer science  |x Mathematics. 
650 0 |a Artificial intelligence. 
650 0 |a Control engineering. 
650 0 |a Robotics. 
650 0 |a Mechatronics. 
650 0 |a Electrical engineering. 
650 1 4 |a Computer Science. 
650 2 4 |a Coding and Information Theory. 
650 2 4 |a Artificial Intelligence (incl. Robotics). 
650 2 4 |a Theory of Computation. 
650 2 4 |a Mathematics of Computing. 
650 2 4 |a Communications Engineering, Networks. 
650 2 4 |a Control, Robotics, Mechatronics. 
700 1 |a Emmert-Streib, Frank.  |e editor. 
700 1 |a Dehmer, Matthias.  |e editor. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9780387848150 
856 4 0 |u http://dx.doi.org/10.1007/978-0-387-84816-7  |z Full Text via HEAL-Link 
912 |a ZDB-2-SCS 
950 |a Computer Science (Springer-11645)