000			03869nam a22005175i 4500
001			978-0-387-84816-7
003			DE-He213
005			20251006084423.0
007			cr nn 008mamaa
008			100301s2009 xxu| s |||| 0|eng d
020			_a9780387848167
020			_a99780387848167
024	7		_a10.1007/978-0-387-84816-7 _2doi
082	0	4	_a004.0151 _223
100	1		_aEmmert-Streib, Frank. _eeditor.
245	1	0	_aInformation Theory and Statistical Learning _h[electronic resource] / _cedited by Frank Emmert-Streib, Matthias Dehmer.
264		1	_aBoston, MA : _bSpringer US, _c2009.
300			_bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aAlgorithmic 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			_aInformation 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	_aCOMPUTER SCIENCE.
650		0	_aINFORMATION THEORY.
650		0	_aARTIFICIAL INTELLIGENCE.
650		0	_aSTATISTICS.
650		0	_aCONTROL ENGINEERING SYSTEMS.
650		0	_aTELECOMMUNICATION.
650	1	4	_aCOMPUTER SCIENCE.
650	2	4	_aTHEORY OF COMPUTATION.
650	2	4	_aARTIFICIAL INTELLIGENCE (INCL. ROBOTICS).
650	2	4	_aMATHEMATICS OF COMPUTING.
650	2	4	_aCOMMUNICATIONS ENGINEERING, NETWORKS.
650	2	4	_aCONTROL , ROBOTICS, MECHATRONICS.
650	2	4	_aSTATISTICS, GENERAL.
700	1		_aDehmer, Matthias. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387848150
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-84816-7 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SCS
942			_2ddc _cER
999			_c59216 _d59216