qInformation Theory and Statistical Learningq 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 qInformation Theory and Statistical Learningq: qA 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.q Shun-ichi Amari, RIKEN Brain Science Institute, Professor-Emeritus at the University of Tokyo... 0.0 1, 013 1, 013 314 310 0.11 8 1.7e+015 0.0 2, 842 2, 842 317 311 0.30 9 4.5e +014 0.0 2, 450 2, 450 317 312 0.28 10 1.1e+014 ... We proved that the optimal pdf obtained from the solution of such a specially-designed MinxEnt program is a anbsp;...
|Title||:||Information Theory and Statistical Learning|
|Author||:||Frank Emmert-Streib, Matthias Dehmer|
|Publisher||:||Springer Science & Business Media - 2008-11-24|