Natural Computing for Unsupervised Learning

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Synopsis

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 techniquesReports 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

Book details

Series:
Unsupervised and Semi-Supervised Learning
Author:
Xiangtao Li, Ka-Chun Wong
ISBN:
9783319985664
Publisher:
Springer International Publishing
Pages:
N/A
Reading age:
Not specified
Includes images:
Yes
Date of addition:
2018-11-04
Usage restrictions:
Copyright
Copyright date:
2019
Copyright by:
Springer International Publishing AG 
Adult content:
No
Language:
English
Categories:
Communication, Computers and Internet, Nonfiction, Technology