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100   $a20230914d2023      u|             0
101 0 $aeng
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181   $ctxt$2rdacontent
182   $cc$2rdamedia
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200 10$aLearning with the Minimum Description Length Principle$b[electronic resource] /$fby Kenji Yamanishi
205   $a1st ed. 2023.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023.
215   $a1 online resource (352 pages)
311 08$aPrint version: Yamanishi, Kenji Learning with the Minimum Description Length Principle Singapore : Springer,c2023 9789819917891 
327   $aInformation and Coding -- Parameter Estimation -- Model Selection -- Latent Variable Model Selection -- Sequential Prediction -- MDL Change Detection -- Continuous Model Selection -- Extension of Stochastic Complexity -- Mathematical Preliminaries.
330   $aThis 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.
606   $aData structures (Computer science)
606   $aInformation theory
606   $aMachine learning
606   $aData Structures and Information Theory
606   $aMachine Learning
615  0$aData structures (Computer science).
615  0$aInformation theory.
615  0$aMachine learning.
615 14$aData Structures and Information Theory.
615 24$aMachine Learning.
676   $a005.73
700   $aYamanishi$b Kenji$01428048
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a996550557803316
996   $aLearning with the Minimum Description Length Principle$93563133
997   $aUNISA