東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Learning with the minimum descriptio...

Yamanishi, Kenji.

Linked to FindBook

Google Book

Amazon

博客來

Learning with the minimum description length principle

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learning with the minimum description length principle/ by Kenji Yamanishi.
Author:	Yamanishi, Kenji.
Published:	Singapore :Springer Nature Singapore : : 2023.,
Description:	xx, 339 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Data structures (Computer science) -
Online resource:	https://doi.org/10.1007/978-981-99-1790-7
ISBN:	9789819917907

Learning with the minimum description length principle
Yamanishi, Kenji.

Learning with the minimum description length principle[electronic resource] /by Kenji Yamanishi. - Singapore :Springer Nature Singapore :2023. - xx, 339 p. :ill., digital ;24 cm.

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.

ISBN: 9789819917907

Standard No.: 10.1007/978-981-99-1790-7doiSubjects--Topical Terms:

527210
Data structures (Computer science)

LC Class. No.: QA76.9.D35

Dewey Class. No.: 005.73

Learning with the minimum description length principle
LDR:02414nmm a2200349 a 4500 001 2334456
003 DE-He213
005 20230914153802.0
006 m d
007 cr nn 008maaau
008 240402s2023 si s 0 eng d
020 $a 9789819917907 $q (electronic bk.)
020 $a 9789819917891 $q (paper)
024 7 $a 10.1007/978-981-99-1790-7 $2 doi
035 $a 978-981-99-1790-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D35
072 7 $a UMB $2 bicssc
072 7 $a GPF $2 bicssc
072 7 $a COM031000 $2 bisacsh
072 7 $a UMB $2 thema
072 7 $a GPF $2 thema
082 0 4 $a 005.73 $2 23
090 $a QA76.9.D35 $b Y19 2023
100 1 $a Yamanishi, Kenji. $3 3666041
245 1 0 $a Learning with the minimum description length principle $h [electronic resource] / $c by Kenji Yamanishi.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a xx, 339 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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 $a 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 $a Data structures (Computer science) $3 527210
650 0 $a Machine learning. $3 533906
650 1 4 $a Data Structures and Information Theory. $3 3382368
650 2 4 $a Machine Learning. $3 3382522
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-99-1790-7
950 $a Computer Science (SpringerNature-11645)