Journal article
Extraction of the relevant delays for temporal modeling
When modeling temporal processes, just like in pattern recognition, selecting the optimal number of inputs is of central concern. We take advantage of specific features of temporal modeling to propose a novel method for extracting the inputs that attempts to yield the best predictive performance. The method relies on the use of estimators of the generalization error to assess the predictive performance of the model.
This technique is first applied to time series processing, where we perform a number of experiments on synthetic data, as well as a real life dataset, and compare the results to a benchmark physical method. Finally, the method is extended to system identification and illustrated by the estimation of a linear FIR filter on functional magnetic resonance (fMRI) signals.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2000 |
| Pages: | 1787-1795 |
| ISSN: | 19410476 and 1053587x |
| Types: | Journal article |
| DOI: | 10.1109/78.845935 |
Approximation error Data mining Delay effects FIR filters Finite impulse response filter Iterative methods Magnetic resonance imaging Pattern recognition Predictive models Signal processing System identification benchmark physical method biomedical MRI delays extraction experiments feature extraction filtering theory functional magnetic resonance signals generalization error linear FIR filter pattern recognition prediction theory predictive performance real life dataset synthetic data system identification temporal modeling temporal processes time series time series processing
