Paper Info
Title
Local coupled extreme learning machine.
Abstract
Due to the significant efficiency and simple implementation, extreme learning machine (ELM) algorithms enjoy much attention in regression and classification applications recently. Many efforts have been paid to enhance the performance of ELM from both methodology (ELM training strategies) and structure (incremental or pruned ELMs) perspectives. In this paper, a local coupled extreme learning machine (LC-ELM) algorithm is presented. By assigning an address to each hidden node in the input space, LC-ELM introduces a decoupler framework to ELM in order to reduce the complexity of the weight searching space. The activated degree of a hidden node is measured by the membership degree of the similarity between the associated address and the given input. Experimental results confirm that the proposed approach works effectively and generally outperforms the original ELM in both regression and classification applications.
Year
DOI
Venue
2016
10.1007/s00521-013-1542-4
Neural Computing and Applications
Keywords
Field
DocType
Extreme learning machine, LC-ELM, Classification, Regression
Regression,Extreme learning machine,Artificial intelligence,Hidden node problem,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
27
1
1433-3058
Citations 
PageRank 
References 
4
0.51
22
Authors
1
Name
Order
Citations
PageRank
Yanpeng Qu1297.46