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Trends in extreme learning machines: A review
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 …
recently. In this review, we aim to report the current state of the theoretical research and …
[PDF][PDF] Extreme learning machine: a review
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 …
learning and they have gained a significantly wide acceptance. However, it was recently …
Extreme learning machines: a survey
Computational intelligence techniques have been used in wide applications. Out of
numerous computational intelligence techniques, neural networks and support vector …
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 …
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 …
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
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 …
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 …
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
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 …
an optimal controller of unknown discrete-time nonlinear systems with control constraints. A …
A recent survey on colon cancer detection techniques
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 …
its detection is histopathological tissue analysis, which, though leads to vital diagnosis, is …
Semi-supervised robust training with generalized perturbed neighborhood
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) …
(DNNs). One of the most effective adversarial defense methods is adversarial training (AT) …
A novel progressive learning technique for multi-class classification
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 …
newly developed learning technique is independent of the number of class constraints and it …