A Binary-Classification-Based Metric between ...
Type de document :
Article dans une revue scientifique
Titre :
A Binary-Classification-Based Metric between Time-Series Distributions and Its Use in Statistical and Learning Problems
Auteur(s) :
Ryabko, Daniil [Auteur]
Sequential Learning [SEQUEL]
Mary, Jérémie [Auteur]
Sequential Learning [SEQUEL]
Sequential Learning [SEQUEL]
Mary, Jérémie [Auteur]
Sequential Learning [SEQUEL]
Titre de la revue :
Journal of Machine Learning Research
Pagination :
2837-2856
Éditeur :
Microtome Publishing
Date de publication :
2013
ISSN :
1532-4435
Discipline(s) HAL :
Informatique [cs]/Intelligence artificielle [cs.AI]
Mathématiques [math]/Statistiques [math.ST]
Statistiques [stat]/Théorie [stat.TH]
Mathématiques [math]/Théorie de l'information et codage [math.IT]
Informatique [cs]/Théorie de l'information [cs.IT]
Mathématiques [math]/Statistiques [math.ST]
Statistiques [stat]/Théorie [stat.TH]
Mathématiques [math]/Théorie de l'information et codage [math.IT]
Informatique [cs]/Théorie de l'information [cs.IT]
Résumé en anglais : [en]
A metric between time-series distributions is proposed that can be evaluated using binary classification methods, which were originally developed to work on i.i.d.\ data. It is shown how this metric can be used for solving ...
Lire la suite >A metric between time-series distributions is proposed that can be evaluated using binary classification methods, which were originally developed to work on i.i.d.\ data. It is shown how this metric can be used for solving statistical problems that are seemingly unrelated to classification and concern highly dependent time series. Specifically, the problems of time-series clustering, homogeneity testing and the three-sample problem are addressed. Universal consistency of the resulting algorithms is proven under most general assumptions. The theoretical results are illustrated with experiments on synthetic and real-world data.Lire moins >
Lire la suite >A metric between time-series distributions is proposed that can be evaluated using binary classification methods, which were originally developed to work on i.i.d.\ data. It is shown how this metric can be used for solving statistical problems that are seemingly unrelated to classification and concern highly dependent time series. Specifically, the problems of time-series clustering, homogeneity testing and the three-sample problem are addressed. Universal consistency of the resulting algorithms is proven under most general assumptions. The theoretical results are illustrated with experiments on synthetic and real-world data.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :