Title :
Scaling of a length scale for regression and prediction
Author_Institution :
Dept. of Inf. Eng., Okayama Univ., Japan
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;
Conference_Titel :
Neural Networks for Signal Processing, 2002. Proceedings of the 2002 12th IEEE Workshop on
Print_ISBN :
0-7803-7616-1
DOI :
10.1109/NNSP.2002.1030029
