A fast constructive learning algorithm for single-hidden-layer neural networks

The gradient-based learning algorithms are usually used to train feedforward neural networks. In these algorithms, the parameters of the network are adjusted iteratively according to the partial gradients of the user-defined performance functions. Such algorithms usually require tens to hundreds of...

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Veröffentlicht in:ICARCV 2004 : 8th Control, Automation, Robotics and Vision Conference, 2004 : 6-9 December 2004 Jg. 3; S. 1907 - 1911 Vol. 3
Hauptverfasser: Qin-Yu Zhu, Guang-Bin Huang, Chee-Kheong Siew
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 2004
Schlagworte:
Artificial neural networks
Backpropagation algorithms
Clustering algorithms
Feedforward neural networks
Iterative algorithms
Joining processes
Machine learning
Neural networks
Neurons
Vectors
ISBN:9780780386532, 0780386531
Online-Zugang:Volltext
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Zusammenfassung:The gradient-based learning algorithms are usually used to train feedforward neural networks. In these algorithms, the parameters of the network are adjusted iteratively according to the partial gradients of the user-defined performance functions. Such algorithms usually require tens to hundreds of learning epochs to reach the required accuracy. If it sticks in the local minimum in the learning process, the situation tends to be even worse. In Huang et al., a novel fast learning algorithm called extreme learning machine (ELM) for single-hidden-layer neural networks (SLFNs) has been proposed where a constructive method is used instead of a gradient-based learning algorithm. In this paper, we further verify the performance of ELM on two benchmark artificial problems.
ISBN:9780780386532
0780386531
DOI:10.1109/ICARCV.2004.1469451