Original Contribution: Stacked generalization
Neural Networks
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Machine Learning
Machine Learning
Neural networks for pattern recognition
Neural networks for pattern recognition
A fast fixed-point algorithm for independent component analysis
Neural Computation
High-order contrasts for independent component analysis
Neural Computation
Independent component analysis: algorithms and applications
Neural Networks
Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Improving Regressors using Boosting Techniques
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Theoretical background for ensemble methods with multivariate decomposition
SMO'09 Proceedings of the 9th WSEAS international conference on Simulation, modelling and optimization
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In this paper we present a novel method for prediction improvement when many models are used. Our aim is to find in the modeling results the common basis components and process them to filter the noises and destructive signals. The basis components are found by blind separation methods like PCA or ICA. The constructive signals are integrated using an inverse system to decomposition or neural network. We check the validity of our methodology on load prediction task.