Information Theory and Statistical Learning

Description:

This interdisciplinary text offers theoretical and practical results of information theoretic methods used in statistical learning. It presents a comprehensive overview of the many different methods that have been developed in numerous contexts. Editor(s): Emmert-Streib, Frank; Dehmer, Matthias. Num Pages: 439 pages, biography. BIC Classification: GPF; PBT; UYQ. Category: (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 25. Weight in Grams: 807. . 2008. Hardback. . . . . Books ship from the US and Ireland. Seller Inventory # V9780387848150

About this title:

Synopsis:

"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

Bibliographic Details

Title: Information Theory and Statistical Learning
Publisher: Springer-Verlag New York Inc.
Publication Date: 2008
Binding: Hardcover
Condition: New