NDLI: Dynamic Extreme Learning Machine and Its Approximation Capability

Content Provider	IEEE Xplore Digital Library
Author	Rui Zhang Yuan Lan Guang-Bin Huang Zong-Ben Xu Yeng Chai Soh
Copyright Year	2013
Abstract	Extreme learning machines (ELMs) have been proposed for generalized single-hidden-layer feedforward networks which need not be neuron alike and perform well in both regression and classification applications. The problem of determining the suitable network architectures is recognized to be crucial in the successful application of ELMs. This paper first proposes a dynamic ELM (D-ELM) where the hidden nodes can be recruited or deleted dynamically according to their significance to network performance, so that not only the parameters can be adjusted but also the architecture can be self-adapted simultaneously. Then, this paper proves in theory that such D-ELM using Lebesgue p-integrable hidden activation functions can approximate any Lebesgue p-integrable function on a compact input set. Simulation results obtained over various test problems demonstrate and verify that the proposed D-ELM does a good job reducing the network size while preserving good generalization performance.
Page Count	12
File Size	717683
Starting Page	2054
Ending Page	2065
File Format	PDF
ISSN	21682267
Volume Number	43
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Approximation methods Machine learning Computer architecture Linear systems Educational institutions Cybernetics Feedforward neural networks universal approximation Dynamic learning extreme learning machine (ELM) feedforward neural networks
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

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