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Advances in machine learning for big...

Dehuri, Satchidananda.

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Advances in machine learning for big data analysis

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advances in machine learning for big data analysis/ edited by Satchidananda Dehuri, Yen-Wei Chen.
other author:	Dehuri, Satchidananda.
Published:	Singapore :Springer Singapore : : 2022.,
Description:	xix, 239 p. :ill., digital ;24 cm.
[NT 15003449]:	Deep Learning for Supervised Learning -- Deep Learning for Unsupervised Learning -- Support Vector Machine for Regression -- Support Vector Machine for Classification -- Decision Tree for Regression -- Higher Order Neural Networks -- Competitive Learning -- Semi-supervised Learning -- Multi-objective Optimization Techniques -- Techniques for Feature Selection/Extraction -- Techniques for Task Relevant Big Data Analysis -- Techniques for Post Processing Task in Big Data Analysis -- Customer Relationship Management.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-981-16-8930-7
ISBN:	9789811689307

Advances in machine learning for big data analysis
Advances in machine learning for big data analysis[electronic resource] /edited by Satchidananda Dehuri, Yen-Wei Chen. - Singapore :Springer Singapore :2022. - xix, 239 p. :ill., digital ;24 cm. - Intelligent systems reference library,v. 2181868-4408 ;. - Intelligent systems reference library ;v. 218..

Deep Learning for Supervised Learning -- Deep Learning for Unsupervised Learning -- Support Vector Machine for Regression -- Support Vector Machine for Classification -- Decision Tree for Regression -- Higher Order Neural Networks -- Competitive Learning -- Semi-supervised Learning -- Multi-objective Optimization Techniques -- Techniques for Feature Selection/Extraction -- Techniques for Task Relevant Big Data Analysis -- Techniques for Post Processing Task in Big Data Analysis -- Customer Relationship Management.

This book focuses on research aspects of ensemble approaches of machine learning techniques that can be applied to address the big data problems. In this book, various advancements of machine learning algorithms to extract data-driven decisions from big data in diverse domains such as the banking sector, healthcare, social media, and video surveillance are presented in several chapters. Each of them has separate functionalities, which can be leveraged to solve a specific set of big data applications. This book is a potential resource for various advances in the field of machine learning and data science to solve big data problems with many objectives. It has been observed from the literature that several works have been focused on the advancement of machine learning in various fields like biomedical, stock prediction, sentiment analysis, etc. However, limited discussions have been carried out on application of advanced machine learning techniques in solving big data problems.

ISBN: 9789811689307

Standard No.: 10.1007/978-981-16-8930-7doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .A38 2022

Dewey Class. No.: 006.31

Advances in machine learning for big data analysis
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245 0 0 $a Advances in machine learning for big data analysis $h [electronic resource] / $c edited by Satchidananda Dehuri, Yen-Wei Chen.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2022.
300 $a xix, 239 p. : $b ill., digital ; $c 24 cm.
490 1 $a Intelligent systems reference library, $x 1868-4408 ; $v v. 218
505 0 $a Deep Learning for Supervised Learning -- Deep Learning for Unsupervised Learning -- Support Vector Machine for Regression -- Support Vector Machine for Classification -- Decision Tree for Regression -- Higher Order Neural Networks -- Competitive Learning -- Semi-supervised Learning -- Multi-objective Optimization Techniques -- Techniques for Feature Selection/Extraction -- Techniques for Task Relevant Big Data Analysis -- Techniques for Post Processing Task in Big Data Analysis -- Customer Relationship Management.
520 $a This book focuses on research aspects of ensemble approaches of machine learning techniques that can be applied to address the big data problems. In this book, various advancements of machine learning algorithms to extract data-driven decisions from big data in diverse domains such as the banking sector, healthcare, social media, and video surveillance are presented in several chapters. Each of them has separate functionalities, which can be leveraged to solve a specific set of big data applications. This book is a potential resource for various advances in the field of machine learning and data science to solve big data problems with many objectives. It has been observed from the literature that several works have been focused on the advancement of machine learning in various fields like biomedical, stock prediction, sentiment analysis, etc. However, limited discussions have been carried out on application of advanced machine learning techniques in solving big data problems.
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700 1 $a Dehuri, Satchidananda. $3 901251
700 1 $a Chen, Yen-Wei. $3 2062809
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856 4 0 $u https://doi.org/10.1007/978-981-16-8930-7
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)