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Natural Computing for Unsupervised Learning

  • Xiangtao Li
  • Ka-Chun Wong

Part of the Unsupervised and Semi-Supervised Learning book series (UNSESUL)

Table of contents

  1. Front Matter
    Pages i-vi
  2. Advances in Natural Computing

  3. Advances in Unsupervised Learning

  4. Natural Computing for Unsupervised Learning

    1. Front Matter
      Pages 145-145
    2. Yugal Kumar, Neeraj Dahiya, Sanjay Malik, Geeta Yadav, Vijendra Singh
      Pages 147-162
    3. Saptarsi Goswami, Sanjay Chakraborty, Priyanka Guha, Arunabha Tarafdar, Aman Kedia
      Pages 213-234
  5. Others

    1. Front Matter
      Pages 235-235
    2. Shamdeep Brar, Tomayess Issa, Sulaiman Ghazi B. Alqahtani
      Pages 237-264
  6. Back Matter
    Pages 265-273

About this book

Introduction

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

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

Editors and affiliations

  • Xiangtao Li
    • 1
  • Ka-Chun Wong
    • 2
  1. 1.Department of Information Sciences and TechnologyNortheast Normal UniversityChangchunChina
  2. 2.City University of Hong KongKowloon TongHong Kong

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-98566-4
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2019
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-98565-7
  • Online ISBN 978-3-319-98566-4
  • Series Print ISSN 2522-848X
  • Series Online ISSN 2522-8498
  • Buy this book on publisher's site
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