Isbn: 9789819917891 - learning with the minimum description length principle (17 resultados)

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Hardcover. Condición: new. Hardcover. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

Condición: New. 1st ed. 2023 edition NO-PA16APR2015-KAP.

Hardcover. Condición: Brand New. 359 pages. 9.25x6.10x9.21 inches. In Stock.

Hardcover. Condición: new. Hardcover. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 360 pp. Englisch.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Introduces readers to a modern theory of the minimum description length (MDL) principleIncludes rich examples of MDL applications to machine learning and data science Written by a pioneer of information-theoretic learning theoryKenj.…

Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 360 pp. Englisch.…

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