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008 | 150903s2009 xxu| o |||| 0|eng d | ||
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_a9780387848167 _99780387848167 |
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024 | 7 |
_a10.1007/9780387848167 _2doi |
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_aMX-SnUAN _bspa _cMX-SnUAN _erda |
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050 | 4 | _aQA75.5-76.95 | |
100 | 1 |
_aEmmert-Streib, Frank. _eeditor. _9303690 |
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245 | 1 | 0 |
_aInformation Theory and Statistical Learning / _cedited by Frank Emmert-Streib, Matthias Dehmer. |
264 | 1 |
_aBoston, MA : _bSpringer US, _c2009. |
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300 | _brecurso en línea. | ||
336 |
_atexto _btxt _2rdacontent |
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_acomputadora _bc _2rdamedia |
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_arecurso en línea _bcr _2rdacarrier |
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_aarchivo de texto _bPDF _2rda |
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500 | _aSpringer eBooks | ||
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 | ||
590 | _aPara consulta fuera de la UANL se requiere clave de acceso remoto. | ||
700 | 1 |
_aDehmer, Matthias. _eeditor. _9303691 |
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710 | 2 |
_aSpringerLink (Servicio en línea) _9299170 |
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776 | 0 | 8 |
_iEdición impresa: _z9780387848150 |
856 | 4 | 0 |
_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-84816-7 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL) |
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