DocumentCode :
2310413
Title :
Extreme learning machine: a new learning scheme of feedforward neural networks
Author :
Huang, Guang-Bin ; Zhu, Qin-Yu ; Siew, Chee-Kheong
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore
Volume :
2
fYear :
2004
fDate :
25-29 July 2004
Firstpage :
985
Abstract :
It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: 1) the slow gradient-based learning algorithms are extensively used to train neural networks, and 2) all the parameters of the networks are tuned iteratively by using such learning algorithms. Unlike these traditional implementations, this paper proposes a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses the input weights and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide the best generalization performance at extremely fast learning speed. The experimental results based on real-world benchmarking function approximation and classification problems including large complex applications show that the new algorithm can produce best generalization performance in some cases and can learn much faster than traditional popular learning algorithms for feedforward neural networks.
Keywords :
feedforward neural nets; function approximation; generalisation (artificial intelligence); gradient methods; learning (artificial intelligence); pattern classification; classification problems; extreme learning machine; function approximation; iterative tuning; learning speed; network parameter tuning; neural network training; single hidden layer feedforward neural networks; slow gradient based learning algorithms; Algorithm design and analysis; Approximation algorithms; Feedforward neural networks; Function approximation; Iterative algorithms; Joining processes; Machine learning; Multi-layer neural network; Neural networks; Neurons;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
ISSN :
1098-7576
Print_ISBN :
0-7803-8359-1
Type :
conf
DOI :
10.1109/IJCNN.2004.1380068
Filename :
1380068
Link To Document :
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