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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Qin-Yu Zhu Guang-Bin Huang Chee-Kheong Siew |
| Copyright Year | 2004 |
| Description | Author affiliation: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore (Qin-Yu Zhu; Guang-Bin Huang; Chee-Kheong Siew) |
| Abstract | 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. |
| Starting Page | 1907 |
| Ending Page | 1911 |
| File Size | 698015 |
| Page Count | 5 |
| File Format | |
| ISBN | 0780386531 |
| DOI | 10.1109/ICARCV.2004.1469451 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-12-06 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neural networks Feedforward neural networks Neurons Vectors Machine learning Artificial neural networks Clustering algorithms Backpropagation algorithms Iterative algorithms Joining processes |
| Content Type | Text |
| Resource Type | Article |
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