Genetic Programming for Image Classification - An Automated Approach to Feature Learning (Paperback, 1st ed. 2021)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Adaptation, Learning, and Optimization, 24
Release date:	February 2022
First published:	2021
Authors:	Ying Bi • Bing Xue • Mengjie Zhang
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	258
Edition:	1st ed. 2021
ISBN-13:	978-3-03-065929-5
Categories:	Books > Computing & IT > Applications of computing > Pattern recognition
LSN:	3-03-065929-1
Barcode:	9783030659295

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