Information theory and statistical learning

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

Tutkimustuotos: KokoomateosScientificvertaisarvioitu

21 Sitaatiot (Scopus)

Abstrakti

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."

AlkuperäiskieliEnglanti
KustantajaSpringer US
Sivumäärä439
ISBN (painettu)9780387848150
DOI - pysyväislinkit
TilaJulkaistu - 2009
Julkaistu ulkoisestiKyllä
OKM-julkaisutyyppiC2 Toimitettu teos

!!ASJC Scopus subject areas

  • Computer Science(all)

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