Metaheuristics for Data Clustering and Image Segmentation: 152 (Intelligent Systems Reference Library, 152) - Tapa dura

Libro 123 de 188: Intelligent Systems Reference Library

Abraham, Ajith; Ramadas, Meera

 
9783030040963: Metaheuristics for Data Clustering and Image Segmentation: 152 (Intelligent Systems Reference Library, 152)

Sinopsis

In this book, differential evolution and its modified variants are applied to the clustering of data and images. Metaheuristics have emerged as potential algorithms for dealing with complex optimization problems, which are otherwise difficult to solve using traditional methods. In this regard, differential evolution is considered to be a highly promising technique for optimization and is being used to solve various real-time problems. The book studies the algorithms in detail, tests them on a range of test images, and carefully analyzes their performance. Accordingly, it offers a valuable reference guide for all researchers, students and practitioners working in the fields of artificial intelligence, optimization and data analytics.


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De la contraportada

In this book, differential evolution and its modified variants are applied to the clustering of data and images. Metaheuristics have emerged as potential algorithms for dealing with complex optimization problems, which are otherwise difficult to solve using traditional methods. In this regard, differential evolution is considered to be a highly promising technique for optimization and is being used to solve various real-time problems. The book studies the algorithms in detail, tests them on a range of test images, and carefully analyzes their performance. Accordingly, it offers a valuable reference guide for all researchers, students and practitioners working in the fields of artificial intelligence, optimization and data analytics.


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Otras ediciones populares con el mismo título

9783030040987: Metaheuristics for Data Clustering and Image Segmentation

Edición Destacada

ISBN 10:  3030040984 ISBN 13:  9783030040987
Editorial: Springer, 2019
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