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Machine learning paradigmstheory and...

Hassanien, Aboul Ella.

Machine learning paradigmstheory and application /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning paradigmsedited by Aboul Ella Hassanien.
Reminder of title:	theory and application /
other author:	Hassanien, Aboul Ella.
Published:	Cham :Springer International Publishing :2019.
Description:	ix, 474 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-030-02357-7
ISBN:	9783030023577$q(electronic bk.)

Machine learning paradigmstheory and application /
Machine learning paradigmstheory and application /[electronic resource] :edited by Aboul Ella Hassanien. - Cham :Springer International Publishing :2019. - ix, 474 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.8011860-949X ;. - Studies in computational intelligence ;v. 216..

Part I: Machine Learning in Feature Selection -- Hybrid Feature Selection Method Based On The Genetic Algorithm And Pearson Correlation Coeﬃcient -- Weighting Attributes and Decision Rules through Rankings and Discretisation Parameters -- Greedy Selection of Attributes to be Discretised -- Part II: Machine Learning in Classification and Ontology -- Machine learning for Enhancement Land Cover and Crop Types Classiﬁcation.

The book focuses on machine learning. Divided into three parts, the first part discusses the feature selection problem. The second part then describes the application of machine learning in the classification problem, while the third part presents an overview of real-world applications of swarm-based optimization algorithms. The concept of machine learning (ML) is not new in the field of computing. However, due to the ever-changing nature of requirements in today's world it has emerged in the form of completely new avatars. Now everyone is talking about ML-based solution strategies for a given problem set. The book includes research articles and expository papers on the theory and algorithms of machine learning and bio-inspiring optimization, as well as papers on numerical experiments and real-world applications.

ISBN: 9783030023577$q(electronic bk.)

Standard No.: 10.1007/978-3-030-02357-7doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .M334 2019

Dewey Class. No.: 006.31

Machine learning paradigmstheory and application /
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245 0 0 $a Machine learning paradigms $h [electronic resource] : $b theory and application / $c edited by Aboul Ella Hassanien.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a ix, 474 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.801
505 0 $a Part I: Machine Learning in Feature Selection -- Hybrid Feature Selection Method Based On The Genetic Algorithm And Pearson Correlation Coeﬃcient -- Weighting Attributes and Decision Rules through Rankings and Discretisation Parameters -- Greedy Selection of Attributes to be Discretised -- Part II: Machine Learning in Classification and Ontology -- Machine learning for Enhancement Land Cover and Crop Types Classiﬁcation.
520 $a The book focuses on machine learning. Divided into three parts, the first part discusses the feature selection problem. The second part then describes the application of machine learning in the classification problem, while the third part presents an overview of real-world applications of swarm-based optimization algorithms. The concept of machine learning (ML) is not new in the field of computing. However, due to the ever-changing nature of requirements in today's world it has emerged in the form of completely new avatars. Now everyone is talking about ML-based solution strategies for a given problem set. The book includes research articles and expository papers on the theory and algorithms of machine learning and bio-inspiring optimization, as well as papers on numerical experiments and real-world applications.
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650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Pattern Recognition. $3 273706
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856 4 0 $u https://doi.org/10.1007/978-3-030-02357-7
950 $a Intelligent Technologies and Robotics (Springer-42732)