Electronic Resource

Genetic Programming for Image Classification

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Penilaian

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This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
1908001292	EB0001898	006.31 Biy g	Central Library (OPAC)	Available

Detail Information

Series Title

xxviii, 258p.:Ill

Call Number

006.31 Biy g

Publisher

Switzerland : Springer Cham., 2021

Collation

xxviii, 258p.:Ill

Language

English

ISBN/ISSN

978-3-030-65927-1

Classification

006.31

Content Type

Ebook

Media Type

-

Carrier Type

online resource

Edition

1

Subject(s)

Pattern recognition systems

Specific Detail Info

-

Statement of Responsibility

BRF

Other version/related

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