Multilayer feedforward networks are universal approximators
Neural Networks
Neural Networks: A Comprehensive Foundation
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Neural Networks for Pattern Recognition
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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ECML '98 Proceedings of the 10th European Conference on Machine Learning
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Multiresponse sparse regression with application to multidimensional scaling
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
A Group Selection Evolutionary Extreme Learning Machine approach for Time-Variant Neural Networks
Proceedings of the 2011 conference on Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets
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
An improved extreme learning machine based on particle swarm optimization
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Applying least angle regression to ELM
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
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Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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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
Minimal learning machine: a new distance-based method for supervised learning
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
A study on the randomness reduction effect of extreme learning machine with ridge regression
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Long-term time series prediction using OP-ELM
Neural Networks
Quantifying the reliability of fault classifiers
Information Sciences: an International Journal
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In this brief, the optimally pruned extreme learning machine (OP-ELM) methodology is presented. It is based on the original extreme learning machine (ELM) algorithm with additional steps to make it more robust and generic. The whole methodology is presented in detail and then applied to several regression and classification problems. Results for both computational time and accuracy (mean square error) are compared to the original ELM and to three other widely used methodologies: multilayer perceptron (MLP), support vector machine (SVM), and Gaussian process (GP). As the experiments for both regression and classification illustrate, the proposed OP-ELM methodology performs several orders of magnitude faster than the other algorithms used in this brief, except the original ELM. Despite the simplicity and fast performance, the OP-ELM is still able to maintain an accuracy that is comparable to the performance of the SVM. A toolbox for the OP-ELM is publicly available online.