Natural Computing for Unsupervised Learning
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Beschreibung
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
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
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
Autor*in
Xiangtao Li
Themen in »Natural Computing for Unsupervised Learning«
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