Extreme Learning Machines 2013: Algorithms and Applications [electronic resource] / edited by Fuchen Sun, Kar-Ann Toh, Manuel Grana Romay, Kezhi Mao.

By: Sun, Fuchen [editor.]Contributor(s): Toh, Kar-Ann [editor.] | Romay, Manuel Grana [editor.] | Mao, Kezhi [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Adaptation, Learning, and Optimization: 16Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: VI, 225 p. 100 illus., 74 illus. in color. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783319047416Subject(s): Engineering | Artificial intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q342Online resources: Click here to access online
Contents:
Freshwater Algal Bloom Prediction by Extreme Learning Machine in Macau Storage Reservoirs -- A Novel Scene Based Robust Video Watermarking Scheme in DWT Domain Using Extreme Learning Machine -- Stochastic Sensitivity Analysis using Extreme Learning Machine.
In: Springer eBooksSummary: 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.  
Tags from this library: No tags from this library for this title. Log in to add tags.
    Average rating: 0.0 (0 votes)
No physical items for this record

Freshwater Algal Bloom Prediction by Extreme Learning Machine in Macau Storage Reservoirs -- A Novel Scene Based Robust Video Watermarking Scheme in DWT Domain Using Extreme Learning Machine -- Stochastic Sensitivity Analysis using Extreme Learning Machine.

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.  

There are no comments on this title.

to post a comment.

Implemented and Maintained by Biju Patnaik Central Library.
For any Suggestions/Query Contact to library or Email: library@nitrkl.ac.in OR bpcl-cir@nitrkl.ac.in. Ph:91+6612462103
Website/OPAC best viewed in Mozilla Browser in 1366X768 Resolution.

Powered by Koha