Extreme learning machines: a survey

By G. Huang, Dianhui Wang, Yuan Lan. International Journal of Machine Learning and Cybernetics, 2011

1,939 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/7975195638f3574ac02975df6f4048558ec1bc96

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In Artificial Intelligence

In Electrical and Electronic Engineering

Citations in the corpus

Trends in extreme learning machines: A review (2015, background): “…Cao et al. (2013), Chang et al. (2010), Decherchi et al. (2013), Li, Wang, et al. (2013), Lu et al. (2013), Pan et al. (2012), Suresh et al. (2009), Wang, Huang, et al. (2011), Zhou et al. (2013), Text classification and understanding Cambria et al. (in press), Poria et al. (2014), Yang and Mao…”
Let a biogeography-based optimizer train your Multi-Layer Perceptron (2014, methodology): “It has been demonstrated that this technique is very efficient for classification problems [25].”
Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next (2022, background): “To overcome some difficulties, various researchers have also tried to investigate shallower network solutions: these can be sparse neural networks, instead of fully connected architectures, or more likely single hidden layers as ELM (Extreme Learning Machine) (Huang et al, 2011).”
Robust 3D Action Recognition with Random Occupancy Patterns (2012, background): “[10] and [11] also employ randomization in the learning process.”
Market impact analysis via deep learned architectures (2018, background): “The kernelized version of ELM is proposed by Huang and Siew [4], which improves ELM’s prediction accuracy to a higher level and has been examined by many real-world data sets [6, 7].”
Autoencoder and Its Various Variants (2018, methodology): “Extreme Learning Machine Autoencoder (ELMAE) The ELMAE is extended from AE by instantiating encoder function and decoder function with ELM network [11-13].”
Extreme learning machine: algorithm, theory and applications (2013, methodology): “Extreme learning machine is used in the fields such as signal processing (Mao 2002), automatic control (Mao and Huang 2005), image processing (Huang et al. 2011), market analysis, aviation and aerospace and medical diagnosis (Shang et al. 2005), etc.”
Foundations and Advances in Deep Learning (2014, background): “These reasons have led to the popularity of ELM recently (Huang et al., 2011).”
3D Shape Segmentation and Labeling via Extreme Learning Machine (2014, neutral): “In these cases, on-line learning is more preferable than batch learning since the former does not need retraining when a new datum is received [HWL11].”