Multilayer Perceptron with Functional Inputs: an Inverse Regression Approach
| dc.creator | Ferré, Louis | |
| dc.creator | Villa, Nathalie | |
| dc.date | 2007-05-02 | |
| dc.date.accessioned | 2026-07-07T07:59:06Z | |
| dc.date.available | 2026-07-07T07:59:06Z | |
| dc.description | Functional data analysis is a growing research field as more and more practical applications involve functional data. In this paper, we focus on the problem of regression and classification with functional predictors: the model suggested combines an efficient dimension reduction procedure [functional sliced inverse regression, first introduced by Ferré & Yao (Statistics, 37, 2003, 475)], for which we give a regularized version, with the accuracy of a neural network. Some consistency results are given and the method is successfully confronted to real-life data. | |
| dc.description | 17 pages | |
| dc.identifier | https://arxiv.org/abs/0705.0211 | |
| dc.identifier | http://arxiv.org/abs/0705.0211 | |
| dc.identifier | Scandinavian Journal of Statistics 33, 4 (12/2006) 807-823 | |
| dc.identifier | doi:10.1111/j.1467-9469.2006.00496.x | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/128245 | |
| dc.subject | Statistics Theory | |
| dc.title | Multilayer Perceptron with Functional Inputs: an Inverse Regression Approach | |
| dc.type | text |