Proceedings ArticleDOI
Extreme learning machine: a new learning scheme of feedforward neural networks
Guang-Bin Huang,Qin-Yu Zhu,Chee-Kheong Siew +2 more
- Vol. 2, pp 985-990
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TLDR
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 is proposed.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.read more
Citations
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Journal ArticleDOI
Extreme Learning Machine for Regression and Multiclass Classification
TL;DR: ELM provides a unified learning platform with a widespread type of feature mappings and can be applied in regression and multiclass classification applications directly and in theory, ELM can approximate any target continuous function and classify any disjoint regions.
Journal ArticleDOI
Universal approximation using incremental constructive feedforward networks with random hidden nodes
TL;DR: This paper proves in an incremental constructive method that in order to let SLFNs work as universal approximators, one may simply randomly choose hidden nodes and then only need to adjust the output weights linking the hidden layer and the output layer.
Journal ArticleDOI
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
TL;DR: The results show that the OS-ELM is faster than the other sequential algorithms and produces better generalization performance on benchmark problems drawn from the regression, classification and time series prediction areas.
Journal ArticleDOI
Extreme learning machines: a survey
TL;DR: A survey on Extreme learning machine (ELM) and its variants, especially on (1) batch learning mode of ELM, (2) fully complex ELm, (3) online sequential ELM; and (4) incremental ELM and (5) ensemble ofELM.
Journal ArticleDOI
Trends in extreme learning machines
TL;DR: In this paper, the authors report the current state of the theoretical research and practical advances on this subject and provide a comprehensive view of these advances in ELM together with its future perspectives.
References
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Yoav Freund,Robert E. Schapire +1 more
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Journal ArticleDOI
Approximation capabilities of multilayer feedforward networks
TL;DR: It is shown that standard multilayer feedforward networks with as few as a single hidden layer and arbitrary bounded and nonconstant activation function are universal approximators with respect to L p (μ) performance criteria, for arbitrary finite input environment measures μ.