Results 201 to 210 of about 98,314 (264)

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

Hybrid prediction method for solar photovoltaic power generation using normal cloud parrot optimization algorithm integrated with extreme learning machine. [PDF]

Sci Rep
Liu H, Cai C, Li P, Tang C, Zhao M, Zheng X, Li Y, Zhao Y, Liu C.   +8 more
europepmc   +1 more source

Empowering an Acute Kidney Injury 3D Graphene-Based Sensor Using Extreme Learning Machine. [PDF]

ACS Omega
Sittihakote N, Danvirutai P, Anutrakulchai S, Tuantranont A, Srichan C.   +4 more
europepmc   +1 more source

Ensemble and Pre-Training Approach for Echo State Network and Extreme Learning Machine Models. [PDF]

Entropy (Basel)
Tang L, Wang J, Wang M, Zhao C.
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   +2 more
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

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

Extreme learning machines: a survey

International Journal of Machine Learning and Cybernetics, 2011
Computational intelligence techniques have been used in wide applications. Out of numerous computational intelligence techniques, neural networks and support vector machines (SVMs) have been playing the dominant roles. However, it is known that both neural networks and SVMs face some challenging issues such as: (1) slow learning speed, (2) trivial ...
Guang-Bin Huang, Dianhui Wang 0001, Yuan Lan   +2 more
openaire   +1 more source

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   +1 more source