Learning with the Minimum Description Length Principle

Name: Learning with the Minimum Description Length Principle
Brand: Springer, Berlin
SKU: 9789819917891
Price: 598.16 PLN
Availability: InStock

Autor: Kenji Yamanishi

Język:

Oprawa: Twarda

Wydawca: Springer, Berlin

Dostępność: Dostępna u dostawcy w małych ilościach

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This book introduces readers to the minimum description length (MDL) principle and its applications...

Informacje o książce

Autor

Kenji Yamanishi

Język

Angielski

Oprawa

Książka - Twarda

Data wydania

2023

strony

360

EAN

9789819917891

Enbook ID

43085635

Wydawca

Springer, Berlin

Waga

680

Wymiary

155 x 235

Pełny opis

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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