Evolutionary extreme learning machine
By Qin-Yu Zhu, A. K. Qin, Ponnuthurai Nagaratnam Suganthan, G. Huang. Pattern Recognition, 2005
817 citations recorded by the source corpus.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/c4f759fbf9c29ffaccbe753e2ee9194681829b03
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In Artificial Intelligence
- Li Deng
- Dong Yu
- Jinyu Li
- Yoshua Bengio
- Xiaodong He
- Jianfeng Gao
- Frank Torsten Bernd Seide
- Yifan Gong
Citations in the corpus
- Determination of Amino Acid Nitrogen in Soy Sauce Using Near Infrared Spectroscopy Combined with Characteristic Variables Selection and Extreme Learning Machine (2013, background): “ELM not only learns much faster with higher generalization performance than the traditional gradient-based learning algorithms but also avoids many difficulties faced by gradient-based learning methods such as stopping criteria, learning rate, learning epochs, and local minima (Zhu et al. 2005).”
- Efficient and effective algorithms for training single-hidden-layer neural networks (2012, methodology): “To reduce the model size, a number of algorithms, such as evolutionary-ELM (Zhu et al. 2005) and enhanced random search based incremental ELM (EI-ELM) (Huang and Chen 2008), have been proposed in the literature.”
- Nondestructive Detection of Soluble Solids Content of Apples from Dielectric Spectra with ANN and Chemometric Methods (2015, background): “That means that ELM can avoid some difficulties faced by gradient-based learning methods (Cao et al. 2012; Zhu et al. 2005).”
- Extreme learning machine: algorithm, theory and applications (2013, methodology): “…higher number of hidden neurons due to the random determination of the input weights and hidden biases, a hybrid learning algorithm was proposed by Zhu et al. (2005) which uses the differential evolutionary algorithm to select the input weights and Moore– Penrose (MP) generalized inverse to…”
- Sales forecasting using extreme learning machine with applications in fashion retailing (2008, methodology): “In the following, we give a concise review of ELM [20,37,47], and the normalization and unnormalization procedures [11,23].”