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Evolutionary machine learning techni...

Aljarah, Ibrahim.

Evolutionary machine learning techniquesalgorithms and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Evolutionary machine learning techniquesedited by Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah.
Reminder of title:	algorithms and applications /
other author:	Mirjalili, Seyedali.
Published:	Singapore :Springer Singapore :2020.
Description:	x, 286 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learningMathematics.
Online resource:	https://doi.org/10.1007/978-981-32-9990-0
ISBN:	9789813299900$q(electronic bk.)

Evolutionary machine learning techniquesalgorithms and applications /
Evolutionary machine learning techniquesalgorithms and applications /[electronic resource] :edited by Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah. - Singapore :Springer Singapore :2020. - x, 286 p. :ill., digital ;24 cm. - Algorithms for intelligent systems,2524-7565. - Algorithms for intelligent systems..

This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields.

ISBN: 9789813299900$q(electronic bk.)

Standard No.: 10.1007/978-981-32-9990-0doiSubjects--Topical Terms:

857106
Machine learning
--Mathematics.

LC Class. No.: Q325.5 / .E965 2020

Dewey Class. No.: 006.31

Evolutionary machine learning techniquesalgorithms and applications /
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520 $a This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields.
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