Learning with the Minimum Description Length Principle (Record no. 184831)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03241nam a22005535i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-981-99-1790-7 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240423130059.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230914s2023 si | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789819917907 |
| -- | 978-981-99-1790-7 |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1007/978-981-99-1790-7 |
| Source of number or code | doi |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | QA76.9.D35 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | Q350-390 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | UMB |
| Source | bicssc |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | GPF |
| Source | bicssc |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM021000 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | UMB |
| Source | thema |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | GPF |
| Source | thema |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.73 |
| Edition number | 23 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 003.54 |
| Edition number | 23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Yamanishi, Kenji. |
| Relator term | author. |
| Relator code | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 245 10 - TITLE STATEMENT | |
| Title | Learning with the Minimum Description Length Principle |
| Medium | [electronic resource] / |
| Statement of responsibility, etc | by Kenji Yamanishi. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2023. |
| 264 #1 - | |
| -- | Singapore : |
| -- | Springer Nature Singapore : |
| -- | Imprint: Springer, |
| -- | 2023. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | XX, 339 p. 51 illus., 48 illus. in color. |
| Other physical details | online resource. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data structures (Computer science). |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Information theory. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine learning. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data Structures and Information Theory. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Machine Learning. |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY | |
| Title | Springer Nature eBook |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Printed edition: |
| International Standard Book Number | 9789819917891 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Printed edition: |
| International Standard Book Number | 9789819917914 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Printed edition: |
| International Standard Book Number | 9789819917921 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://doi.org/10.1007/978-981-99-1790-7">https://doi.org/10.1007/978-981-99-1790-7</a> |
| 912 ## - | |
| -- | ZDB-2-SCS |
| 912 ## - | |
| -- | ZDB-2-SXCS |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks-CSE-Springer |
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