Results 241 to 250 of about 8,140,064 (302)

Skin cancer detection through attention guided dual autoencoder approach with extreme learning machine. [PDF]

Sci Rep
Maurya R, Mahapatra S, Dutta MK, Singh VP, Karnati M, Sahu G, Pandey NN.   +6 more
europepmc   +1 more source

Foot fractures diagnosis using a deep convolutional neural network optimized by extreme learning machine and enhanced snow ablation optimizer. [PDF]

Sci Rep
Guo X, Tan C, Shi L, Khishe M, Bagi K.
europepmc   +1 more source

A novel hybrid methodology for wind speed and solar irradiance forecasting based on improved whale optimized regularized extreme learning machine. [PDF]

Sci Rep
Syama S, Ramprabhakar J, Anand R, Meena VP, Guerrero JM.   +4 more
europepmc   +1 more source

Ensemble Extreme Learning Machine Method for Hemoglobin Estimation Based on PhotoPlethysmoGraphic Signals. [PDF]

Sensors (Basel)
Peng F, Zhang N, Chen C, Wu F, Wang W.
europepmc   +1 more source

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Evolutionary extreme learning machine

Pattern Recognition, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guang-Bin Huang, A K Qin, P N Suganthan
exaly   +2 more sources

Extreme ensemble of extreme learning machines

Statistical Analysis and Data Mining: The ASA Data Science Journal, 2020
AbstractExtreme learning machine (ELM) has attracted attentions in pattern classification problems due to its preferences in low computations and high generalization. To overcome its drawbacks, caused by the randomness of input weights and biases, the ensemble of ELMs was proposed.
Eghbal G. Mansoori, Massar Sara
openaire   +1 more source

In-Materio Extreme Learning Machines

2022
Nanomaterial networks have been presented as a building block for unconventional in-Materio processors. Evolution in-Materio (EiM) has previously presented a way to congure and exploit physical materials for computation, but their ability to scale as datasets get larger and more complex remains unclear.
Benedict A. H. Jones, Noura Al Moubayed, Dagou A. Zeze, Chris Groves 0001   +3 more
openaire   +2 more sources

Learning to Rank with Extreme Learning Machine

Neural Processing Letters, 2013
Relevance ranking has been a popular and interesting topic over the years, which has a large variety of applications. A number of machine learning techniques were successfully applied as the learning algorithms for relevance ranking, including neural network, regularized least square, support vector machine and so on.
Weiwei Zong, Guang-Bin Huang
openaire   +1 more source

Trends in extreme learning machines: A review

Neural Networks, 2015
Extreme learning machine (ELM) has gained increasing interest from various research fields recently. In this review, we aim to report the current state of the theoretical research and practical advances on this subject. We first give an overview of ELM from the theoretical perspective, including the interpolation theory, universal approximation ...
Shiji Song, Guang-Bin Huang, Gao Huang
exaly   +4 more sources

Robust extreme learning machine

Neurocomputing, 2013
The output weights computing of extreme learning machine (ELM) encounters two problems, the computational and outlier robustness problems. The computational problem occurs when the hidden layer output matrix is a not full column rank matrix or an ill-conditioned matrix because of randomly generated input weights and biases. An existing solution to this
Punyaphol Horata, Sirapat Chiewchanwattana, Khamron Sunat   +2 more
openaire   +2 more sources