Machine Learning
High-order contrasts for independent component analysis
Neural Computation
Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Blind Source Separation Using Temporal Predictability
Neural Computation
Smooth component analysis as ensemble method for prediction improvement
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
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The paper is addressed to economic problems for which many different models can be proposed. In such situation the ensemble approach is natural way to improve the final prediction results. In particular, we present the method for the prediction improvement with ensemble method based on the multivariate decompositions. As a method for model results decomposition we present the smooth component analysis. The resulting components are classified as destructive and removed, or as constructive and recomposed. The classification of the components is based on the theoretical analysis of MSE error measure. The robustness of the method is validated through practical experiment of energy load consumption in Poland.