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Learning with the Minimum Description Length Principle / by Kenji Yamanishi

データ種別	電子ブック
版	1st ed. 2023.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2023
大きさ	XX, 339 p. 51 illus., 48 illus. in color : online resource
著者標目	*Yamanishi, Kenji author SpringerLink (Online service)

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URL	巻　次	配架場所	請求記号	登録番号	コメント	刷　年	状　態	利用注記	ISBN	予約	請求メモ
URL		射水-電子	007	EB0004400	Computer Scinece R0 2005-6,2022-3				9789819917907

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一般注記	Information and Coding -- Parameter Estimation -- Model Selection -- Latent Variable Model Selection -- Sequential Prediction -- MDL Change Detection -- Continuous Model Selection -- Extension of Stochastic Complexity -- Mathematical Preliminaries 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 HTTP:URL=https://doi.org/10.1007/978-981-99-1790-7
件　名	LCSH:Data structures (Computer science) LCSH:Information theory LCSH:Machine learning FREE:Data Structures and Information Theory FREE:Machine Learning
分　類	LCC:QA76.9.D35 LCC:Q350-390 DC23:005.73 DC23:003.54
書誌ID	EB00003788
ISBN	9789819917907