Signal processing with alpha-stable distributions and applications
Signal processing with alpha-stable distributions and applications
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Machine Learning
Discrete Random Signals and Statistical Signal Processing
Discrete Random Signals and Statistical Signal Processing
Detection of Signals in Noise
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Advanced Digital Signal Processing and Noise Reduction
Advanced Digital Signal Processing and Noise Reduction
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
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
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In this paper we propose a divergence based method for noise detection in ensemble method context where the prediction results from different models are treated as a multidimensional variable that contains constructive and destructive latent components. The crucial stage is the proper destructive and constructive components classification. We propose to calculate the noisiness of the particular latent component as the divergence from chosen reference noise. It allows us to identify the wide range of noises besides the typical signals with close analytical form such as Gaussian or uniform. The real data experiment with load energy prediction confirms presented methodology.