| Type de publication : |
 |
Articles dans des revues avec comité de lecture |
|
| Domaine : |
|
|
|
| Titre : |
|
On the kernel rule for function classification |
|
| Auteur(s) : |
|
Christophe Abraham 1, Gérard Biau 2, 3, Benoît Cadre ( ) 4 |
|
| Laboratoire : |
|
|
|
| Résumé : |
|
Let X be a random variable taking values in a function space F, and let Y be a discrete random label with values 0 and 1. We investigate asymptotic properties of the moving window classification rule based on independent copies of the pair (X, Y ). Contrary to the finite dimensional case, it is shown that the moving window classifier is not universally consistent in the sense that its probability of error may not converge to the Bayes risk for some distributions of (X, Y ). Sufficient conditions both on the space F and the distribution of X are then given to ensure consistency. |
|
Langue du texte
intégral : |
|
Anglais |
|
|
| Journal : |
|
Annals of the institute of mathematical statistics |
|
| Audience : |
|
internationale |
|
| Date de publication : |
|
2006 |
|
| Page, identifiant, ... : |
|
619-633 |
|
|
Récupérer au format
