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Könyv Learning with the Minimum Description Length Principle Kenji Yamanishi

Learning with the Minimum Description Length Principle

Szerző: Kenji Yamanishi
Nyelv: Angol
Kötés: Kemény kötésű
Kiadó: Springer, Berlin
Elérhetőség: Beszállítói készleten
Küldés 10-13 napon belül
47 920 Ft
This book introduces readers to the minimum description length (MDL) principle and its applications...

Információk a könyvről

Szerző
Kenji Yamanishi
Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
2023
oldal
360
EAN
9789819917891
Enbook ID
43085635
Kiadó
Springer, Berlin
Súly
680
Méretek
155 x 235

Teljes leírás

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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