Optimism in Active Learning with Gaussian Processes

Collet, Timothé; Pietquin, Olivier

Document type :

Communication dans un congrès avec actes

Title :

Optimism in Active Learning with Gaussian Processes

Author(s) :

Collet, Timothé [Auteur]
MAchine Learning and Interactive Systems [MALIS]
Georgia Tech Lorraine [Metz]
Pietquin, Olivier [Auteur]
Université de Lille, Sciences et Technologies
Institut universitaire de France [IUF]
Sequential Learning [SEQUEL]

Conference title :

22nd International Conference on Neural Information Processing (ICONIP2015)

City :

Istanbul

Country :

Turquie

Start date of the conference :

2015-11-09

English keyword(s) :

Active Learning
Classifiaction
Multi-Armed Bandits

HAL domain(s) :

Informatique [cs]/Apprentissage [cs.LG]

English abstract : [en]

In the context of Active Learning for classification, the classification error depends on the joint distribution of samples and their labels which is initially unknown. The minimization of this error requires estimating ...
Show more >In the context of Active Learning for classification, the classification error depends on the joint distribution of samples and their labels which is initially unknown. The minimization of this error requires estimating this distribution. Online estimation of this distribution involves a trade-off between exploration and exploitation. This is a common problem in machine learning for which multi-armed bandit theory, building upon Optimism in the Face of Uncertainty, has been proven very efficient these last years. We introduce two novel algorithms that use Optimism in the Face of Uncertainty along with Gaussian Processes for the Active Learning problem. The evaluation lead on real world datasets shows that these new algorithms compare positively to state-of-the-art methods.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

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