Introduction to non-linear optimization
Introduction to non-linear optimization
Machine Learning
Neural networks for pattern recognition
Neural networks for pattern recognition
Discrete Random Signals and Statistical Signal Processing
Discrete Random Signals and Statistical Signal Processing
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
Theoretical background for ensemble methods with multivariate decomposition
SMO'09 Proceedings of the 9th WSEAS international conference on Simulation, modelling and optimization
Noise detection for ensemble methods
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
The noise identification method based on divergence analysis in ensemble methods context
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
| Hi-index | 0.00 |
In this paper we derive a novel smooth component analysis algorithm applied 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. The filtration of those destructive components and proper mixing of those constructive should improve final prediction results. The filtration process can be performed by neural networks with initial weights computed from smooth component analysis. The validity and high performance of our concept is presented on the real problem of energy load prediction.