國立高雄大學圖資館 |

Language: English

Back

Ensemble methods :foundations and algorithms /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Ensemble methods :Zhi-Hua Zhou.
Reminder of title:	foundations and algorithms /
Author:	Zhou, Zhi-Hua
Published:	Boca Raton, FL :CRC Press,2025.
Description:	xv, 347 p. :ill. ;25 cm.
Subject:	Multiple comparisons (Statistics)
ISBN:	9781032960609 :

Ensemble methods :foundations and algorithms /
Zhou, Zhi-Hua(Computer scientist)

Ensemble methods :foundations and algorithms /Zhi-Hua Zhou. - 2nd ed. - Boca Raton, FL :CRC Press,2025. - xv, 347 p. :ill. ;25 cm. - Chapman & Hall/CRC machine learning & pattern recognition. - Chapman & Hall/CRC machine learning & pattern recognition..

Includes bibliographical references and index.

"Ensemble methods that train multiple learners and then combine them to use, with \textit{Boosting} and \textit{Bagging} as representatives, are well-known machine learning approaches. It has become common sense that an ensemble is usually significantly more accurate than a single learner, and ensemble methods have already achieved great success in various real-world tasks. Twelve years have passed since the publication of the first edition of the book in 2012 (Japanese and Chinese versions published in 2017 and 2020, respectively). Many significant advances in this field have been developed. First, many theoretical issues have been tackled, e.g., the fundamental question of \textit{why AdaBoost seems resistant to overfitting} gets addressed, so that now we understand much more about the essence of ensemble methods. Second, ensemble methods have been well developed in more machine learning fields, e.g., \textit{isolation forest} in anomaly detection, so that now we have powerful ensemble methods for tasks beyond conventional supervised learning. Third, ensemble mechanisms have also been found helpful in phenomenon and emerging areas such as deep learning and online learning. Therefore, it is time to present the second edition of the book. The book is intended to be written in a concise but comprehensive style, not to be too lengthy to make readers who just step into this field feel frightened. Due to the significantly increased amount of content, however, the current book is nearly half thicker than its anterior edition"--

ISBN: 9781032960609 :NT$2552

LCCN: 2024041213Subjects--Topical Terms:

377560
Multiple comparisons (Statistics)

LC Class. No.: QA278.4 / .Z47 2025

Dewey Class. No.: 006.3/1

Ensemble methods :foundations and algorithms /
LDR:02296cam a2200229 a 4500 001 694703
005 20250607131614.9
008 261007s2025 flua b 001 0 eng
010 $a 2024041213
020 $a 9781032960609 : $c NT$2552
020 $a 9781032960616 (pbk.)
020 $z 9781003587774 (ebk.)
035 $a 23895165
040 $a DLC $b eng $c DLC
050 0 0 $a QA278.4 $b .Z47 2025
082 0 0 $a 006.3/1 $2 23/eng/20241129
100 1 $a Zhou, Zhi-Hua $c (Computer scientist) $e author. $3 1011704
245 1 0 $a Ensemble methods : $b foundations and algorithms / $c Zhi-Hua Zhou.
250 $a 2nd ed.
260 $a Boca Raton, FL : $b CRC Press, $c 2025.
300 $a xv, 347 p. : $b ill. ; $c 25 cm.
490 1 $a Chapman & Hall/CRC machine learning & pattern recognition
504 $a Includes bibliographical references and index.
520 $a "Ensemble methods that train multiple learners and then combine them to use, with \textit{Boosting} and \textit{Bagging} as representatives, are well-known machine learning approaches. It has become common sense that an ensemble is usually significantly more accurate than a single learner, and ensemble methods have already achieved great success in various real-world tasks. Twelve years have passed since the publication of the first edition of the book in 2012 (Japanese and Chinese versions published in 2017 and 2020, respectively). Many significant advances in this field have been developed. First, many theoretical issues have been tackled, e.g., the fundamental question of \textit{why AdaBoost seems resistant to overfitting} gets addressed, so that now we understand much more about the essence of ensemble methods. Second, ensemble methods have been well developed in more machine learning fields, e.g., \textit{isolation forest} in anomaly detection, so that now we have powerful ensemble methods for tasks beyond conventional supervised learning. Third, ensemble mechanisms have also been found helpful in phenomenon and emerging areas such as deep learning and online learning. Therefore, it is time to present the second edition of the book. The book is intended to be written in a concise but comprehensive style, not to be too lengthy to make readers who just step into this field feel frightened. Due to the significantly increased amount of content, however, the current book is nearly half thicker than its anterior edition"-- $c Provided by publisher.
650 0 $a Multiple comparisons (Statistics) $3 377560
650 0 $a Set theory. $3 190892
650 0 $a Mathematical analysis. $3 186133
830 0 $a Chapman & Hall/CRC machine learning & pattern recognition. $3 976554