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Analytic learning methods for pattern recognition

Record Type:	Electronic resources : Monograph/item
Title/Author:	Analytic learning methods for pattern recognitionby 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.
Contained By:	Springer Nature eBook
Subject:	Pattern recognition systems.
Online resource:	https://doi.org/10.1007/978-981-96-2151-4
ISBN:	9789819621514$q(electronic bk.)

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$q(electronic bk.)

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

183725
Pattern recognition systems.

LC Class. No.: TK7882.P3

Dewey Class. No.: 006.4

Analytic learning methods for pattern recognition
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245 0 0 $a Analytic learning methods for pattern recognition $h [electronic resource] / $c by Kar-Ann Toh ... [et al.].
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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 183725
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Automated Pattern Recognition. $3 913155
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Artificial Intelligence. $3 212515
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