IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Letters: Convex incremental extreme learning machine
Neurocomputing
Deterministic neural classification
Neural Computation
Maximizing area under ROC curve for biometric scores fusion
Pattern Recognition
Universal Approximation and QoS Violation Application of Extreme Learning Machine
Neural Processing Letters
Ensembling Extreme Learning Machines
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
A Robust Online Sequential Extreme Learning Machine
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
An Improved On-Line Sequential Learning Algorithm for Extreme Learning Machine
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Integrated Analytic Framework for Neural Network Construction
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Design and Implementation of a General Purpose Neural Network Processor
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Active Noise Control Using a Feedforward Network with Online Sequential Extreme Learning Machine
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
A machine learning methodology for the analysis of workplace accidents
International Journal of Computer Mathematics - Recent Advances in Computational and Applied Mathematics in Science and Engineering
Convergence analysis of convex incremental neural networks
Annals of Mathematics and Artificial Intelligence
Sales forecasting using extreme learning machine with applications in fashion retailing
Decision Support Systems
Incremental constructive ridgelet neural network
Neurocomputing
Evolutionary product-unit neural networks classifiers
Neurocomputing
Neural Network with Matrix Inputs
Informatica
Brief paper: An adaptive optimization scheme with satisfactory transient performance
Automatica (Journal of IFAC)
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
IEEE Transactions on Circuits and Systems Part I: Regular Papers
The graph neural network model
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Large scale nonlinear control system fine-tuning through learning
IEEE Transactions on Neural Networks
Error minimized extreme learning machine with growth of hidden nodes and incremental learning
IEEE Transactions on Neural Networks
Almost Random Projection Machine
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Efficient Parametric Adjustment of Fuzzy Inference System Using Error Backpropagation Method
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
ACC'09 Proceedings of the 2009 conference on American Control Conference
A constructive enhancement for online sequential extreme learning machine
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A division algebraic framework for multidimensional support vector regression
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
OP-ELM: optimally pruned extreme learning machine
IEEE Transactions on Neural Networks
A new online learning algorithm for structure-adjustable extreme learning machine
Computers & Mathematics with Applications
Engineering Applications of Artificial Intelligence
Constructive approximation to multivariate function by decay RBF neural network
IEEE Transactions on Neural Networks
Two-stage extreme learning machine for regression
Neurocomputing
Incremental-based extreme learning machine algorithms for time-variant neural networks
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Ordinal extreme learning machine
Neurocomputing
Approximation capability of interpolation neural networks
Neurocomputing
The multidimensional function approximation based on constructive wavelet RBF neural network
Applied Soft Computing
Application of extreme learning machine for series compensated transmission line protection
Engineering Applications of Artificial Intelligence
ELM-Based time-variant neural networks with incremental number of output basis functions
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
Face recognition based on kernelized extreme learning machine
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
Batch intrinsic plasticity for extreme learning machines
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Weighting Efficient Accuracy and Minimum Sensitivity for Evolving Multi-Class Classifiers
Neural Processing Letters
Voting based extreme learning machine
Information Sciences: an International Journal
Channel equalization using complex extreme learning machine with RBF kernels
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Enhanced extreme learning machine with modified gram-schmidt algorithm
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Orthogonal least squares based on singular value decomposition for spare basis selection
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Evolutionary learning using a sensitivity-accuracy approach for classification
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Novel weighting in single hidden layer feedforward neural networks for data classification
Computers & Mathematics with Applications
Letters: Random optimized geometric ensembles
Neurocomputing
A new automatic target recognition system based on wavelet extreme learning machine
Expert Systems with Applications: An International Journal
Modeling spectral data based on mutual information and kernel extreme learning machines
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Applying least angle regression to ELM
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
A Computer Aided Diagnosis System for Thyroid Disease Using Extreme Learning Machine
Journal of Medical Systems
Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs
Knowledge-Based Systems
Weighted extreme learning machine for imbalance learning
Neurocomputing
Robust extreme learning machine
Neurocomputing
Displacement prediction model of landslide based on ensemble of extreme learning machine
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
A multi-objective micro genetic ELM algorithm
Neurocomputing
PCA-ELM: A Robust and Pruned Extreme Learning Machine Approach Based on Principal Component Analysis
Neural Processing Letters
Parallel Chaos Search Based Incremental Extreme Learning Machine
Neural Processing Letters
Extreme learning machine: a robust modeling technique? yes!
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Extending extreme learning machine with combination layer
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Generalized single-hidden layer feedforward networks
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Neural networks letter: Comments on the "No-Prop" algorithm
Neural Networks
Comparison of different approaches to visual terrain classification for outdoor mobile robots
Pattern Recognition Letters
Long-term time series prediction using OP-ELM
Neural Networks
Meta-ELM: ELM with ELM hidden nodes
Neurocomputing
Clustering in extreme learning machine feature space
Neurocomputing
Fast sparse approximation of extreme learning machine
Neurocomputing
Genetic ensemble of extreme learning machine
Neurocomputing
Hybrid extreme rotation forest
Neural Networks
Learning to Rank with Extreme Learning Machine
Neural Processing Letters
Applications of Hybrid Extreme Rotation Forests for image segmentation
International Journal of Hybrid Intelligent Systems
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According to conventional neural network theories, single-hidden-layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes are universal approximators when all the parameters of the networks are allowed adjustable. However, as observed in most neural network implementations, tuning all the parameters of the networks may cause learning complicated and inefficient, and it may be difficult to train networks with nondifferential activation functions such as threshold networks. Unlike conventional neural network theories, this paper proves in an incremental constructive method that in order to let SLFNs work as universal approximators, one may simply randomly choose hidden nodes and then only need to adjust the output weights linking the hidden layer and the output layer. In such SLFNs implementations, the activation functions for additive nodes can be any bounded nonconstant piecewise continuous functions g:R→R and the activation functions for RBF nodes can be any integrable piecewise continuous functions g:R→R and ∫Rg(x)dx≠0. The proposed incremental method is efficient not only for SFLNs with continuous (including nondifferentiable) activation functions but also for SLFNs with piecewise continuous (such as threshold) activation functions. Compared to other popular methods such a new network is fully automatic and users need not intervene the learning process by manually tuning control parameters.