Overview
- 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
Part of the book series: Unsupervised and Semi-Supervised Learning (UNSESUL)
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Table of contents (11 chapters)
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Front Matter
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Advances in Natural Computing
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Front Matter
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Advances in Unsupervised Learning
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Front Matter
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Natural Computing for Unsupervised Learning
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Front Matter
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Others
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Front Matter
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Back Matter
Keywords
- Evolutionary Programming
- Differential Evolution
- Artificial Immune Systems
- Ant Colony Optimization
- Self-organizing Systems
- Evolutionary Multi-objective Optimization
- Runtime Analysis of Natural Computing
- DNA Computing
- Fuzzy Logic / Rough Set Theory
- Artificial Neural Networks
- Convolutional Neural Networks
- Deep Neural Networks
- Ensemble Approaches
- Nature-Inspired Clustering
- Theoretical Foundation Topics
- Big Data Challenges
- Engineering Applications
- Real-World Application
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
Editors and Affiliations
About the editors
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.
Bibliographic Information
Book Title: Natural Computing for Unsupervised Learning
Editors: Xiangtao Li, Ka-Chun Wong
Series Title: Unsupervised and Semi-Supervised Learning
DOI: https://doi.org/10.1007/978-3-319-98566-4
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2019
Hardcover ISBN: 978-3-319-98565-7Published: 12 November 2018
Softcover ISBN: 978-3-030-07508-8Published: 14 December 2018
eBook ISBN: 978-3-319-98566-4Published: 31 October 2018
Series ISSN: 2522-848X
Series E-ISSN: 2522-8498
Edition Number: 1
Number of Pages: VI, 273
Number of Illustrations: 42 b/w illustrations, 79 illustrations in colour
Topics: Communications Engineering, Networks, Signal, Image and Speech Processing, Pattern Recognition, Artificial Intelligence, Data Mining and Knowledge Discovery