Name: Learning with the Minimum Description Length Principle
SKU: 64792333
Price: 111.47 USD
Availability: InStock
Author: Kenji Yamanishi
ISBN: 9789819917921

portada Learning with the Minimum Description Length Principle

Learning with the Minimum Description Length Principle

Formato

Libro Físico

Editorial

Springer Verlag, Singapore

Autor

Kenji Yamanishi

Año

2024

N° páginas

339

Encuadernación

Tapa Blanda

Dimensiones

9.25x6.1 in

ISBN13

9789819917921

Learning with the Minimum Description Length Principle

Kenji Yamanishi (Autor) · Springer Verlag, Singapore · Tapa Blanda

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Reseña del libro "Learning with the Minimum Description Length Principle"

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.