Adaptation, Learning, and Optimization

Extreme Learning Machines 2013: Algorithms and Applications

Editors: Sun, F., Toh, K.-A., Romay, M.G., Mao, K. (Eds.)

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  • Explains the most recent developments of Extreme Learning Machines
  • Includes theories and algorithms such as universal approximation and convergence, robustness and stability analysis, real-time learning/reasoning, sequential and incremental learning, and kernel based algorithms
  • Proceedings of the International Conference on Extreme Learning Machines (ELM2013), Beijing, October 15-17, 2013
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eBook 93,08 €
price for Spain (gross)
  • ISBN 978-3-319-04741-6
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Hardcover 145,59 €
price for Spain (gross)
Softcover 116,63 €
price for Spain (gross)
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About this book

In recent years, ELM has emerged as a revolutionary technique of computational intelligence, and has attracted considerable attentions. An extreme learning machine (ELM) is a single layer feed-forward neural network alike learning system, whose connections from the input layer to the hidden layer are randomly generated, while the connections from the hidden layer to the output layer are learned through linear learning methods. The outstanding merits of extreme learning machine (ELM) are its fast learning speed, trivial human intervene and high scalability.

This book contains some selected papers from the International Conference on Extreme Learning Machine 2013, which was held in Beijing China, October 15-17, 2013. This conference aims to bring together the researchers and practitioners of extreme learning machine from a variety of fields including artificial intelligence, biomedical engineering and bioinformatics, system modelling and control, and signal and image processing, to promote research and discussions of “learning without iterative tuning".

This book covers algorithms and applications of ELM. It gives readers a glance of the newest developments of ELM.

Table of contents (15 chapters)

Table of contents (15 chapters)
  • Stochastic Sensitivity Analysis Using Extreme Learning Machine

    Pages 1-12

    Becerra-Alonso, David (et al.)

  • Efficient Data Representation Combining with ELM and GNMF

    Pages 13-23

    Zeng, Zhiyong (et al.)

  • Extreme Support Vector Regression

    Pages 25-34

    Zhu, Wentao (et al.)

  • A Modular Prediction Mechanism Based on Sequential Extreme Learning Machine with Application to Real-Time Tidal Prediction

    Pages 35-53

    Yin, Jian-Chuan (et al.)

  • An Improved Weight Optimization and Cholesky Decomposition Based Regularized Extreme Learning Machine for Gene Expression Data Classification

    Pages 55-66

    Wei, ShaSha (et al.)

Buy this book

eBook 93,08 €
price for Spain (gross)
  • ISBN 978-3-319-04741-6
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • Immediate eBook download after purchase and usable on all devices
  • Bulk discounts from 10 eBooks
Hardcover 145,59 €
price for Spain (gross)
Softcover 116,63 €
price for Spain (gross)
Rent the eBook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Extreme Learning Machines 2013: Algorithms and Applications
Editors
  • Fuchen Sun
  • Kar-Ann Toh
  • Manuel Grana Romay
  • Kezhi Mao
Series Title
Adaptation, Learning, and Optimization
Series Volume
16
Copyright
2014
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing Switzerland
eBook ISBN
978-3-319-04741-6
DOI
10.1007/978-3-319-04741-6
Hardcover ISBN
978-3-319-04740-9
Softcover ISBN
978-3-319-35003-5
Series ISSN
1867-4534
Edition Number
1
Number of Pages
VI, 225
Number of Illustrations
26 b/w illustrations, 74 illustrations in colour
Topics