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

Linked to FindBook

Google Book

Amazon

博客來

Analytic learning methods for pattern recognition

Record Type:	Electronic resources : Monograph/item
Title/Author:	Analytic learning methods for pattern recognition/ by Kar-Ann Toh ... [et al.].
other author:	Toh, Kar-Ann.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xx, 387 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	Introduction -- Data Preprocessing -- Linear Parametric Models -- Learning Score Functions -- Analytic Learning -- Penalized Learning -- Network Learning -- Ensemble Learning -- Performance Evaluation -- Applications.
Contained By:	Springer Nature eBook
Subject:	Pattern recognition systems. -
Online resource:	https://doi.org/10.1007/978-981-96-2151-4
ISBN:	9789819621514

Analytic learning methods for pattern recognition
Analytic learning methods for pattern recognition[electronic resource] /by Kar-Ann Toh ... [et al.]. - Singapore :Springer Nature Singapore :2025. - xx, 387 p. :ill. (chiefly color), digital ;24 cm.

Introduction -- Data Preprocessing -- Linear Parametric Models -- Learning Score Functions -- Analytic Learning -- Penalized Learning -- Network Learning -- Ensemble Learning -- Performance Evaluation -- Applications.

This textbook is a consolidation of learning methods which comes in an analytic form. The covered learning methods include classical and advanced solutions to problems of regression, minimum classification error, maximum receiver operating characteristics, bridge regression, ensemble learning and network learning. Both the primal and dual solution forms are discussed for over-and under-determined systems. Such coverage provides an important perspective for handling systems with overwhelming samples or systems with overwhelming parameters. For goal driven classification, the solutions to minimum classification-error, maximum receiver operating characteristics, bridge regression, and ensemble learning represent recent advancements in the literature. In this book, the exercises offer instructors and students practical experience with real-world applications.

ISBN: 9789819621514

Standard No.: 10.1007/978-981-96-2151-4doiSubjects--Topical Terms:

527885
Pattern recognition systems.

LC Class. No.: TK7882.P3

Dewey Class. No.: 006.4

Analytic learning methods for pattern recognition
LDR:02094nmm a2200325 a 4500 001 2410362
003 DE-He213
005 20250531130243.0
006 m d
007 cr nn 008maaau
008 260204s2025 si s 0 eng d
020 $a 9789819621514 $q (electronic bk.)
020 $a 9789819621507 $q (paper)
024 7 $a 10.1007/978-981-96-2151-4 $2 doi
035 $a 978-981-96-2151-4
040 $a GP $c GP
041 0 $a eng
050 4 $a TK7882.P3
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.4 $2 23
090 $a TK7882.P3 $b A532 2025
245 0 0 $a Analytic learning methods for pattern recognition $h [electronic resource] / $c by Kar-Ann Toh ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2025.
300 $a xx, 387 p. : $b ill. (chiefly color), digital ; $c 24 cm.
505 0 $a Introduction -- Data Preprocessing -- Linear Parametric Models -- Learning Score Functions -- Analytic Learning -- Penalized Learning -- Network Learning -- Ensemble Learning -- Performance Evaluation -- Applications.
520 $a This textbook is a consolidation of learning methods which comes in an analytic form. The covered learning methods include classical and advanced solutions to problems of regression, minimum classification error, maximum receiver operating characteristics, bridge regression, ensemble learning and network learning. Both the primal and dual solution forms are discussed for over-and under-determined systems. Such coverage provides an important perspective for handling systems with overwhelming samples or systems with overwhelming parameters. For goal driven classification, the solutions to minimum classification-error, maximum receiver operating characteristics, bridge regression, and ensemble learning represent recent advancements in the literature. In this book, the exercises offer instructors and students practical experience with real-world applications.
650 0 $a Pattern recognition systems. $3 527885
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Automated Pattern Recognition. $3 3538549
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Toh, Kar-Ann. $3 1573331
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-96-2151-4
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)