Overview
- Introduces readers to a modern theory of the minimum description length (MDL) principle
- Includes rich examples of MDL applications to machine learning and data science
- Written by a pioneer of information-theoretic learning theory
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About this book
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.Keywords
Table of contents (9 chapters)
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Learning with the Minimum Description Length Principle
Authors: Kenji Yamanishi
DOI: https://doi.org/10.1007/978-981-99-1790-7
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2023
Hardcover ISBN: 978-981-99-1789-1Published: 15 September 2023
Softcover ISBN: 978-981-99-1792-1Published: 16 September 2024
eBook ISBN: 978-981-99-1790-7Published: 14 September 2023
Edition Number: 1
Number of Pages: XX, 339
Number of Illustrations: 3 b/w illustrations, 48 illustrations in colour
Topics: Data Structures and Information Theory, Machine Learning