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Nature-inspired computation in data ...

Yang, Xin-She.

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Nature-inspired computation in data mining and machine learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nature-inspired computation in data mining and machine learning/ edited by Xin-She Yang, Xing-Shi He.
other author:	Yang, Xin-She.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xi, 273 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Natural computation. -
Online resource:	https://doi.org/10.1007/978-3-030-28553-1
ISBN:	9783030285531

Nature-inspired computation in data mining and machine learning
Nature-inspired computation in data mining and machine learning[electronic resource] /edited by Xin-She Yang, Xing-Shi He. - Cham :Springer International Publishing :2020. - xi, 273 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.8551860-949X ;. - Studies in computational intelligence ;v.855..

This book reviews the latest developments in nature-inspired computation, with a focus on the cross-disciplinary applications in data mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their importance in both theory and practical applications. Adopting an application-focused approach, each chapter introduces a specific topic, with detailed descriptions of relevant algorithms, extensive literature reviews and implementation details. Covering topics such as nature-inspired algorithms, swarm intelligence, classification, clustering, feature selection, cybersecurity, learning algorithms over cloud, extreme learning machines, object categorization, particle swarm optimization, flower pollination and firefly algorithms, and neural networks, it also presents case studies and applications, including classifications of crisis-related tweets, extraction of named entities in the Tamil language, performance-based prediction of diseases, and healthcare services. This book is both a valuable a reference resource and a practical guide for students, researchers and professionals in computer science, data and management sciences, artificial intelligence and machine learning.

ISBN: 9783030285531

Standard No.: 10.1007/978-3-030-28553-1doiSubjects--Topical Terms:

1002233
Natural computation.

LC Class. No.: QA76.9.N37 / N37 2020

Dewey Class. No.: 006.38

Nature-inspired computation in data mining and machine learning
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300 $a xi, 273 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.855
520 $a This book reviews the latest developments in nature-inspired computation, with a focus on the cross-disciplinary applications in data mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their importance in both theory and practical applications. Adopting an application-focused approach, each chapter introduces a specific topic, with detailed descriptions of relevant algorithms, extensive literature reviews and implementation details. Covering topics such as nature-inspired algorithms, swarm intelligence, classification, clustering, feature selection, cybersecurity, learning algorithms over cloud, extreme learning machines, object categorization, particle swarm optimization, flower pollination and firefly algorithms, and neural networks, it also presents case studies and applications, including classifications of crisis-related tweets, extraction of named entities in the Tamil language, performance-based prediction of diseases, and healthcare services. This book is both a valuable a reference resource and a practical guide for students, researchers and professionals in computer science, data and management sciences, artificial intelligence and machine learning.
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