Leseprobe
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Natural Computing for Unsupervised Learning
Xiangtao Li (Hrsg.), Ka-Chun Wong (Hrsg.)
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Springer International Publishing 
Naturwissenschaften, Medizin, Informatik, Technik / Elektronik, Elektrotechnik, Nachrichtentechnik
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
Kundenbewertungen
Evolutionary Multi-objective Optimization, Self-organizing Systems, Theoretical Foundation Topics, Engineering Applications, Nature-Inspired Clustering, Convolutional Neural Networks, Big Data Challenges, Ensemble Approaches, Evolutionary Programming, Fuzzy Logic / Rough Set Theory, Artificial Immune Systems, Differential Evolution, Ant Colony Optimization, Runtime Analysis of Natural Computing, Artificial Neural Networks, Deep Neural Networks, Real-World Application, DNA Computing