Title :

Predicting the outcomes of every process for which an asymptotically accurate stationary predictor exists is impossible

Author(s) :

Ryabko, Daniil [Auteur]
Sequential Learning [SEQUEL]
Ryabko, Boris [Auteur]

Conference title :

International Symposium on Information Theory

City :

Hong Kong

Country :

Hong Kong

Start date of the conference :

2015-06

Publisher :

IEEE

HAL domain(s) :

Mathématiques [math]/Théorie de l'information et codage [math.IT]
Informatique [cs]/Apprentissage [cs.LG]
Statistiques [stat]
Statistiques [stat]/Théorie [stat.TH]
Informatique [cs]/Recherche d'information [cs.IR]

English abstract : [en]

The problem of prediction consists in forecasting the conditional distribution of the next outcome given the past. Assume that the source generating the data is such that there is a stationary predictor whose error converges ...
Show more >The problem of prediction consists in forecasting the conditional distribution of the next outcome given the past. Assume that the source generating the data is such that there is a stationary predictor whose error converges to zero (in a certainsense). The question is whether there is a universal predictor for all such sources, that is, a predictor whose error goes to zero if any of the sources that have this property is chosen to generate the data. This question is answered in the negative, contrasting a number of previously established positive results concerning related but smaller sets of processes.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL