Natural Computing for Unsupervised Learning / edited by Xiangtao Li, Ka-Chun Wong.

Format:

Book

Contributor:

Li, Xiangtao, 1960- editor.

Wong, Ka-Chun, editor.

SpringerLink (Online service)

Series:

Engineering (Springer-11647)

Unsupervised and semi-supervised learning 2522-848X

Unsupervised and Semi-Supervised Learning, 2522-848X

Language:

English

Subjects (All):

Electrical engineering.

Signal processing.

Image processing.

Speech processing systems.

Pattern perception.

Artificial intelligence.

Data mining.

Communications Engineering, Networks.

Signal, Image and Speech Processing.

Pattern Recognition.

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Local Subjects:

Communications Engineering, Networks.

Signal, Image and Speech Processing.

Pattern Recognition.

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Physical Description:

1 online resource (VI, 273 pages) : 121 illustrations, 79 illustrations in color.

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-319-98566-4

9783319985664

OCLC:

1180315867

Access Restriction:

Restricted for use by site license.