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Natural computing for unsupervised l...

Li, Xiangtao.

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Natural computing for unsupervised learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Natural computing for unsupervised learning/ edited by Xiangtao Li, Ka-Chun Wong.
other author:	Li, Xiangtao.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	vi, 273 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction -- Part I - Basic Natural Computing Techniques for Unsupervised Learning -- Hard Clustering using Evolutionary Algorithms -- Soft Clustering using Evolutionary Algorithms -- Fuzzy / Rough Set Systems for Unsupervised Learning -- Unsupervised Feature Selection using Evolutionary Algorithms -- Unsupervised Feature Selection using Artificial Neural Networks -- Part II - Advanced Natural Computing Techniques for Unsupervised Learning -- Hybrid Genetic Algorithms for Feature Subset Selection in Model-Based Clustering -- Nature-Inspired Optimization Approaches for Unsupervised Feature Selection -- Co-Evolutionary Approaches for Unsupervised Learning -- Mining Evolving Patterns using Natural Computing Techniques -- Multi-objective Optimization for Unsupervised Learning -- Many-objective Optimization for Unsupervised Learning -- Part III - Applications -- Unsupervised Identification of DNA-binding Proteins using Natural Computing Techniques -- Parallel Solution-based Natural Clustering Techniques on Railway Engineering data -- Natural Computing Techniques for Community Detection on Online Social Networks -- Big Data Challenges and Scalability in Natural Computing for Unsupervised Learning -- Conclusion.
Contained By:	Springer eBooks
Subject:	Natural computation. -
Online resource:	https://doi.org/10.1007/978-3-319-98566-4
ISBN:	9783319985664

Natural computing for unsupervised learning
Natural computing for unsupervised learning[electronic resource] /edited by Xiangtao Li, Ka-Chun Wong. - Cham :Springer International Publishing :2019. - vi, 273 p. :ill. (some col.), digital ;24 cm. - Unsupervised and semi-supervised learning,2522-848X. - Unsupervised and semi-supervised learning..

Introduction -- Part I - Basic Natural Computing Techniques for Unsupervised Learning -- Hard Clustering using Evolutionary Algorithms -- Soft Clustering using Evolutionary Algorithms -- Fuzzy / Rough Set Systems for Unsupervised Learning -- Unsupervised Feature Selection using Evolutionary Algorithms -- Unsupervised Feature Selection using Artificial Neural Networks -- Part II - Advanced Natural Computing Techniques for Unsupervised Learning -- Hybrid Genetic Algorithms for Feature Subset Selection in Model-Based Clustering -- Nature-Inspired Optimization Approaches for Unsupervised Feature Selection -- Co-Evolutionary Approaches for Unsupervised Learning -- Mining Evolving Patterns using Natural Computing Techniques -- Multi-objective Optimization for Unsupervised Learning -- Many-objective Optimization for Unsupervised Learning -- Part III - Applications -- Unsupervised Identification of DNA-binding Proteins using Natural Computing Techniques -- Parallel Solution-based Natural Clustering Techniques on Railway Engineering data -- Natural Computing Techniques for Community Detection on Online Social Networks -- Big Data Challenges and Scalability in Natural Computing for Unsupervised Learning -- Conclusion.

This book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum computing, DNA computing, and others. The book also includes information on the use of natural computing techniques for unsupervised learning tasks. It features several trending topics, such as big data scalability, wireless network analysis, engineering optimization, social media, and complex network analytics. It shows how these applications have triggered a number of new natural computing techniques to improve the performance of unsupervised learning methods. With this book, the readers can easily capture new advances in this area with systematic understanding of the scope in depth. Readers can rapidly explore new methods and new applications at the junction between natural computing and unsupervised learning. Includes advances on unsupervised learning using natural computing techniques Reports on topics in emerging areas such as evolutionary multi-objective unsupervised learning Features natural computing techniques such as evolutionary multi-objective algorithms and many-objective swarm intelligence algorithms.

ISBN: 9783319985664

Standard No.: 10.1007/978-3-319-98566-4doiSubjects--Topical Terms:

1002233
Natural computation.

LC Class. No.: QA76.9.N37 / N388 2019

Dewey Class. No.: 006.3

Natural computing for unsupervised learning
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245 0 0 $a Natural computing for unsupervised learning $h [electronic resource] / $c edited by Xiangtao Li, Ka-Chun Wong.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a vi, 273 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Unsupervised and semi-supervised learning, $x 2522-848X
505 0 $a Introduction -- Part I - Basic Natural Computing Techniques for Unsupervised Learning -- Hard Clustering using Evolutionary Algorithms -- Soft Clustering using Evolutionary Algorithms -- Fuzzy / Rough Set Systems for Unsupervised Learning -- Unsupervised Feature Selection using Evolutionary Algorithms -- Unsupervised Feature Selection using Artificial Neural Networks -- Part II - Advanced Natural Computing Techniques for Unsupervised Learning -- Hybrid Genetic Algorithms for Feature Subset Selection in Model-Based Clustering -- Nature-Inspired Optimization Approaches for Unsupervised Feature Selection -- Co-Evolutionary Approaches for Unsupervised Learning -- Mining Evolving Patterns using Natural Computing Techniques -- Multi-objective Optimization for Unsupervised Learning -- Many-objective Optimization for Unsupervised Learning -- Part III - Applications -- Unsupervised Identification of DNA-binding Proteins using Natural Computing Techniques -- Parallel Solution-based Natural Clustering Techniques on Railway Engineering data -- Natural Computing Techniques for Community Detection on Online Social Networks -- Big Data Challenges and Scalability in Natural Computing for Unsupervised Learning -- Conclusion.
520 $a This book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum computing, DNA computing, and others. The book also includes information on the use of natural computing techniques for unsupervised learning tasks. It features several trending topics, such as big data scalability, wireless network analysis, engineering optimization, social media, and complex network analytics. It shows how these applications have triggered a number of new natural computing techniques to improve the performance of unsupervised learning methods. With this book, the readers can easily capture new advances in this area with systematic understanding of the scope in depth. Readers can rapidly explore new methods and new applications at the junction between natural computing and unsupervised learning. Includes advances on unsupervised learning using natural computing techniques Reports on topics in emerging areas such as evolutionary multi-objective unsupervised learning Features natural computing techniques such as evolutionary multi-objective algorithms and many-objective swarm intelligence algorithms.
650 0 $a Natural computation. $3 1002233
650 0 $a Machine learning. $3 533906
650 0 $a Self-organizing systems. $3 556938
650 0 $a Database management. $3 527442
650 1 4 $a Communications Engineering, Networks. $3 891094
650 2 4 $a Signal, Image and Speech Processing. $3 891073
650 2 4 $a Pattern Recognition. $3 891045
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
700 1 $a Li, Xiangtao. $3 3380851
700 1 $a Wong, Ka-Chun. $3 3166390
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Unsupervised and semi-supervised learning. $3 3380848
856 4 0 $u https://doi.org/10.1007/978-3-319-98566-4
950 $a Engineering (Springer-11647)