Machine Learning
Neural networks for pattern recognition
Neural networks for pattern recognition
High-order contrasts for independent component analysis
Neural Computation
Discrete Random Signals and Statistical Signal Processing
Discrete Random Signals and Statistical Signal Processing
Detection of Signals in Noise
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
Prediction improvement via smooth component analysis and neural network mixing
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
| Hi-index | 0.00 |
In this paper we present a novel noisy signal identification method applied in ensemble methods for destructive components classification. Typically two main signal properties like variability and predictability are described by the same second order statistic characteristic. In our approach we postulate to separate measure of the signal internal dependencies and their variability. The validity of the approach is confirmed by the experiment with energy load data.