Information theory and statistical learning

Frank Emmert-Streib (Editor), Matthias Dehmer (Editor)

Research output: Book/ReportAnthologyScientificpeer-review

21 Citations (Scopus)

Abstract

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."

Original languageEnglish
PublisherSpringer US
Number of pages439
ISBN (Print)9780387848150
DOIs
Publication statusPublished - 2009
Externally publishedYes
Publication typeC2 Edited book

ASJC Scopus subject areas

  • Computer Science(all)

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