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Carte Learning with the Minimum Description Length Principle Kenji Yamanishi

Learning with the Minimum Description Length Principle

Autor: Kenji Yamanishi
Limbă: engleză
Legare: Copertă tare
Editura: Springer, Berlin
Disponibilitate: În depozitul extern
Expediem în 10-13 zile
688.66 lei
This book introduces readers to the minimum description length (MDL) principle and its applications...

Informații despre carte

Autor
Kenji Yamanishi
Limbă
engleză
Legare
Carte - Copertă tare
Publicat
2023
Pagini
360
EAN
9789819917891
Enbook ID
43085635
Editura
Springer, Berlin
Greutate
680
Dimensiuni
155 x 235

Descriere completă

This book introduces readers to the minimum description length (MDL) principle and its applications in learning. The MDL is a fundamental principle for inductive inference, which is used in many applications including statistical modeling, pattern recognition and machine learning. At its core, the MDL is based on the premise that "the shortest code length leads to the best strategy for learning anything from data." The MDL provides a broad and unifying view of statistical inferences such as estimation, prediction and testing and, of course, machine learning.The content covers the theoretical foundations of the MDL and broad practical areas such as detecting changes and anomalies, problems involving latent variable models, and high dimensional statistical inference, among others. The book offers an easy-to-follow guide to the MDL principle, together with other information criteria, explaining the differences between their standpoints. Written in a systematic, concise and comprehensive style, this book is suitable for researchers and graduate students of machine learning, statistics, information theory and computer science.

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