Introduction to non-linear optimization
Introduction to non-linear optimization
Machine Learning
High-order contrasts for independent component analysis
Neural Computation
Discrete Random Signals and Statistical Signal Processing
Discrete Random Signals and Statistical Signal Processing
Machine Learning
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
Theoretical background for ensemble methods with multivariate decomposition
SMO'09 Proceedings of the 9th WSEAS international conference on Simulation, modelling and optimization
Smooth component analysis and MSE decomposition for ensemble methods
KES-AMSTA'12 Proceedings of the 6th KES international conference on Agent and Multi-Agent Systems: technologies and applications
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In this paper we apply a novel smooth component analysis algorithm as ensemble method for prediction improvement. When many prediction models are tested we can treat their results as multivariate variable with the latent components having constructive or destructive impact on prediction results. We show that elimination of those destructive components and proper mixing of those constructive can improve the final prediction results. The validity and high performance of our concept is presented on the problem of energy load prediction.