Supervised functional classification: A theoretical remark and some comparisons
| dc.creator | Baillo, Amparo | |
| dc.creator | Cuevas, Antonio | |
| dc.date | 2008-06-17 | |
| dc.date.accessioned | 2026-07-07T09:45:05Z | |
| dc.date.available | 2026-07-07T09:45:05Z | |
| dc.description | The problem of supervised classification (or discrimination) with functional data is considered, with a special interest on the popular k-nearest neighbors (k-NN) classifier. First, relying on a recent result by Cerou and Guyader (2006), we prove the consistency of the k-NN classifier for functional data whose distribution belongs to a broad family of Gaussian processes with triangular covariance functions. Second, on a more practical side, we check the behavior of the k-NN method when compared with a few other functional classifiers. This is carried out through a small simulation study and the analysis of several real functional data sets. While no global "uniform" winner emerges from such comparisons, the overall performance of the k-NN method, together with its sound intuitive motivation and relative simplicity, suggests that it could represent a reasonable benchmark for the classification problem with functional data. | |
| dc.description | 18 pages | |
| dc.identifier | https://arxiv.org/abs/0806.2831 | |
| dc.identifier | http://arxiv.org/abs/0806.2831 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/163088 | |
| dc.subject | Machine Learning | |
| dc.subject | 62G07 | |
| dc.title | Supervised functional classification: A theoretical remark and some comparisons | |
| dc.type | text |