Trends in extreme learning machines: A review

G Huang, GB Huang, S Song, K You - Neural Networks, 2015‏ - Elsevier
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 …

[PDF][PDF] Extreme learning machine: a review

MAA Albadra, S Tiuna - International Journal of Applied Engineering …, 2017‏ - academia.edu
Feedforward neural networks (FFNN) have been utilised for various research in machine
learning and they have gained a significantly wide acceptance. However, it was recently …

Extreme learning machines: a survey

GB Huang, DH Wang, Y Lan - … journal of machine learning and cybernetics, 2011‏ - Springer
Computational intelligence techniques have been used in wide applications. Out of
numerous computational intelligence techniques, neural networks and support vector …

Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during …

F Weng, H Zhang, C Yang - Resources Policy, 2021‏ - Elsevier
The outbreak of news and opinions during the COVID-19 pandemic is unprecedented in this
age of rapid dissemination of information. The ensuing uncertainty has led to the emergence …

Fault detection and diagnosis of non-linear non-Gaussian dynamic processes using kernel dynamic independent component analysis

J Fan, Y Wang - Information Sciences, 2014‏ - Elsevier
This paper proposes a novel approach for dealing with fault detection of multivariate
processes, which will be referred to as kernel dynamic independent component analysis …

Residual compensation extreme learning machine for regression

J Zhang, W **ao, Y Li, S Zhang - Neurocomputing, 2018‏ - Elsevier
Extreme learning machine (ELM) was proposed for training single hidden layer feedforward
neural networks (SLFNs), and can provide an efficient learning solution for regression …

An iterative adaptive dynamic programming algorithm for optimal control of unknown discrete-time nonlinear systems with constrained inputs

D Liu, D Wang, X Yang - Information Sciences, 2013‏ - Elsevier
In this paper, the adaptive dynamic programming (ADP) approach is employed for designing
an optimal controller of unknown discrete-time nonlinear systems with control constraints. A …

A recent survey on colon cancer detection techniques

S Rathore, M Hussain, A Ali… - IEEE/ACM Transactions …, 2013‏ - ieeexplore.ieee.org
Colon cancer causes deaths of about half a million people every year. Common method of
its detection is histopathological tissue analysis, which, though leads to vital diagnosis, is …

Semi-supervised robust training with generalized perturbed neighborhood

Y Li, B Wu, Y Feng, Y Fan, Y Jiang, Z Li, ST **a - Pattern Recognition, 2022‏ - Elsevier
Adversarial examples have been shown to be a severe threat to deep neural networks
(DNNs). One of the most effective adversarial defense methods is adversarial training (AT) …

A novel progressive learning technique for multi-class classification

R Venkatesan, MJ Er - Neurocomputing, 2016‏ - Elsevier
In this paper, a progressive learning technique for multi-class classification is proposed. This
newly developed learning technique is independent of the number of class constraints and it …