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Unsupervised and Semi-Supervised Learning

Natural Computing for Unsupervised Learning

Editors: Li, Xiangtao, Wong, Ka-Chun (Eds.)

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  • 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
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eBook $129.00
price for USA in USD (gross)
  • ISBN 978-3-319-98566-4
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $169.99
price for USA in USD
  • ISBN 978-3-319-98565-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $169.99
price for USA in USD
  • ISBN 978-3-030-07508-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

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

About the authors

Xiangtao Li received the B.Eng. Degree, the M.Eng. and Ph.D. degrees in computer science from Northeast Normal University, Changchun, China in 2009, 2012, 2015, respectively. Now He is an associate professor in the Department of Computer science and information technology, Northeast Normal University. He has published more than 50 research papers. His research interests include intelligent computation, evolutionary data mining, constrained optimization, bioinformatics, computational biology and interdisciplinary research.
Ka-Chun Wong received the BEng degree in computer engineering from United College, Chinese University of Hong Kong, in 2008. He received the MPhil degree from the same university in 2010 and the PhD degree from the Department of Computer Science, University of Toronto in 2014. He assumed his duty as an assistant professor at City University of Hong Kong in 2015. His research interests include bioinformatics, computational biology, evolutionary computation, data mining, machine learning, and interdisciplinary research. He is merited as the associate editor of BioData Mining in 2016. In addition, he is on the editorial board of Applied Soft Computing since 2016. He has solely edited 2 books published by Springer and CRC Press, attracting 30 peer-reviewed book chapters around the world.

Table of contents (11 chapters)

Table of contents (11 chapters)
  • Detailed Modeling of CSC-STATCOM with Optimized PSO Based Controller

    Pages 3-34

    Gupta, Sandeep

  • A Brief Review and Comparative Study of Nature-Inspired Optimization Algorithms Applied to Power System Control

    Pages 35-49

    Kouba, Nour E. L. Yakine (et al.)

  • Self-Organization: A Perspective on Applications in the Internet of Things

    Pages 51-64

    Ahmed, Furqan

  • Applications of Unsupervised Techniques for Clustering of Audio Data

    Pages 67-99

    Shetty, Surendra (et al.)

  • Feature Extraction and Classification in Brain-Computer Interfacing: Future Research Issues and Challenges

    Pages 101-131

    Chakladar, Debashis Das (et al.)

Buy this book

eBook $129.00
price for USA in USD (gross)
  • ISBN 978-3-319-98566-4
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $169.99
price for USA in USD
  • ISBN 978-3-319-98565-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $169.99
price for USA in USD
  • ISBN 978-3-030-07508-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Natural Computing for Unsupervised Learning
Editors
  • Xiangtao Li
  • Ka-Chun Wong
Series Title
Unsupervised and Semi-Supervised Learning
Copyright
2019
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG, part of Springer Nature
eBook ISBN
978-3-319-98566-4
DOI
10.1007/978-3-319-98566-4
Hardcover ISBN
978-3-319-98565-7
Softcover ISBN
978-3-030-07508-8
Series ISSN
2522-848X
Edition Number
1
Number of Pages
VI, 273
Number of Illustrations
42 b/w illustrations, 79 illustrations in colour
Topics