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Analytic learning methods for pattern recognition

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Analytic learning methods for pattern recognitionby Kar-Ann Toh ... [et al.].
其他作者:	Toh, Kar-Ann.
出版者:	Singapore :Springer Nature Singapore :2025.
面頁冊數:	xx, 387 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Pattern recognition systems.
電子資源:	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

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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.
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650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Automated Pattern Recognition. $3 913155
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650 2 4 $a Artificial Intelligence. $3 212515
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