國立虎尾科技大學 |

Analytic learning methods for pattern recognition

Record Type:	Language materials, printed : 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.;
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:

557384
Pattern recognition systems.

LC Class. No.: TK7882.P3

Dewey Class. No.: 006.4

Analytic learning methods for pattern recognition
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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 557384
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Automated Pattern Recognition. $3 1365734
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Artificial Intelligence. $3 646849
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