NDLI: Self-adaptive extreme learning machine

Content Provider	Springer Nature Link
Author	Wang, Gai Ge Lu, Mei Dong, Yong Quan Zhao, Xiang Jun
Copyright Year	2015
Abstract	In order to overcome the disadvantage of the traditional algorithm for SLFN (single-hidden layer feedforward neural network), an improved algorithm for SLFN, called extreme learning machine (ELM), is proposed by Huang et al. However, ELM is sensitive to the neuron number in hidden layer and its selection is a difficult-to-solve problem. In this paper, a self-adaptive mechanism is introduced into the ELM. Herein, a new variant of ELM, called self-adaptive extreme learning machine (SaELM), is proposed. SaELM is a self-adaptive learning algorithm that can always select the best neuron number in hidden layer to form the neural networks. There is no need to adjust any parameters in the training process. In order to prove the performance of the SaELM, it is used to solve the Italian wine and iris classification problems. Through the comparisons between SaELM and the traditional back propagation, basic ELM and general regression neural network, the results have proven that SaELM has a faster learning speed and better generalization performance when solving the classification problem.
Starting Page	291
Ending Page	303
Page Count	13
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	27
Issue Number	2
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2015-03-21
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Classification Self-adaptive Extreme learning machine Back propagation General regression neural network Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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