DocumentCode
2199185
Title
Scaling of a length scale for regression and prediction
Author
Aida, Toshiaki
Author_Institution
Dept. of Inf. Eng., Okayama Univ., Japan
fYear
2002
fDate
2002
Firstpage
179
Lastpage
187
Abstract
We analyze the prediction from noised data, based on a regression formulation of the problem. For the regression, we construct a model with a length scale to smooth the data, which is determined by the variance of noise and the speed of the variation of original signals. The model is found to be effective also for prediction. This is because it decreases an uncertain region near a boundary as the speed of the variation of original signals increases, which is a crucial property for accurate prediction.
Keywords
Gaussian noise; prediction theory; smoothing methods; splines (mathematics); statistical analysis; Bayesian formulation; Gaussian noise; continuous spline; data smoothing; information processing; noise variance; noised data; numerical simulations; prediction; regression formulation; smoothing length scale; time series prediction; Algorithm design and analysis; Elementary particles; Fractals; Gaussian noise; Information analysis; Information processing; Performance analysis; Predictive models; Sampling methods; Shape;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks for Signal Processing, 2002. Proceedings of the 2002 12th IEEE Workshop on
Print_ISBN
0-7803-7616-1
Type
conf
DOI
10.1109/NNSP.2002.1030029
Filename
1030029
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