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portada Learning with the Minimum Description Length Principle
Type
Physical Book
Author
Publisher
Language
English
Pages
339
Format
Hardcover
Dimensions
23.4x15.6x2.1 cm
Weight
0.68 kg.
ISBN13
9789819917891
Categories

Learning with the Minimum Description Length Principle

Kenji Yamanishi (Author) · Springer · Hardcover

Learning with the Minimum Description Length Principle - Yamanishi, Kenji

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Synopsis "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.

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