Natural Computing for Unsupervised Learning (Unsupervised and Semi-Supervised Learning) - Hardcover

 
9783319985657: Natural Computing for Unsupervised Learning (Unsupervised and Semi-Supervised Learning)
Hardcover
ISBN 10: 3319985655 ISBN 13: 9783319985657
Verlag: Springer, 2018

Inhaltsangabe

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

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Über die Autorin bzw. den Autor

Xiangtao Li received the B.Eng. Degree, the M.Eng. and Ph.D. degrees in computer science from Northeast Normal University, Changchun, China in 2009, 2012, 2015, respectively. Now He is an associate professor in the Department of Computer science and information technology, Northeast Normal University. He has published more than 50 research papers. His research interests include intelligent computation, evolutionary data mining, constrained optimization, bioinformatics, computational biology and interdisciplinary research.

Ka-Chun Wong received the BEng degree in computer engineering from United College, Chinese University of Hong Kong, in 2008. He received the MPhil degree from the same university in 2010 and the PhD degree from the Department of Computer Science, University of Toronto in 2014. He assumed his duty as an assistant professor at City University of Hong Kong in 2015. His research interests include bioinformatics, computational biology, evolutionary computation, data mining, machine learning, and interdisciplinary research. He is merited as the associate editor of BioData Mining in 2016. In addition, he is on the editorial board of Applied Soft Computing since 2016. He has solely edited 2 books published by Springer and CRC Press, attracting 30 peer-reviewed book chapters around the world.

Von der hinteren Coverseite

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

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

Verlag
Springer
Erscheinungsdatum
2018
Sprache
Englisch
ISBN 10 
3319985655
ISBN 13 
9783319985657
Einband
Gebundene Ausgabe
Auflage
1
Anzahl der Seiten
279
Herausgeber
Li Xiangtao, Wong Ka-Chun
Kontakt zum Hersteller
preigu GmbH & Co. KG
mail@preigu.de

Lengericher Landstr. 19
Osnabrück
Niedersachsen
49078
Deutschland

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9783030075088: Natural Computing for Unsupervised Learning (Unsupervised and Semi-Supervised Learning)

Vorgestellte Ausgabe

ISBN 10:  3030075087 ISBN 13:  9783030075088
Verlag: Springer, 2018
Softcover