Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Recursive Lazy Learning for Modeling and Control
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Direct Method of Nonparametric Measurement Selection
IEEE Transactions on Computers
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
Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Two-stage extreme learning machine for regression
Neurocomputing
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
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
| Hi-index | 0.00 |
This paper presents the Optimally-Pruned Extreme Learning Machine (OP-ELM) toolbox. This novel, fast and accurate methodology is applied to several regression and classification problems. The results are compared with widely known Multilayer Perceptron (MLP) and Least-Squares Support Vector Machine (LS-SVM) methods. As the experiments (regression and classification) demonstrate, the OP-ELM methodology is considerably faster than the MLP and the LS-SVM, while maintaining the accuracy in the same level. Finally, a toolbox performing the OP-ELM is introduced and instructions are presented.