Reference : On-the-fly domain adaptation of binary classifiers
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/2268/166536
On-the-fly domain adaptation of binary classifiers
English
Pierard, Sébastien mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
Marcos Alvarez, Alejandro mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Lejeune, Antoine mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Exploitation des signaux et images >]
Van Droogenbroeck, Marc mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
6-Jun-2014
23rd Belgian-Dutch Conference on Machine Learning (BENELEARN)
20-28
Yes
No
International
23rd Belgian-Dutch Conference on Machine Learning (BENELEARN)
06-06-2014
Brussels
Belgium
[en] Classification ; On-the-fly ; Domain adaptation
[en] This work considers the on-the-fly domain adaptation of supervised binary classifiers, learned off-line, in order to adapt them to a target context. The probability density functions associated to negative and positive classes are supposed to be mixtures of the source distributions. Moreover, the mixture weights and the priors are only available at runtime. We present a theoretical solution to this problem, and demonstrate the effectiveness of the proposed approach on a real computer vision application. Our theoretical solution is applicable to any classifier approximating Bayes' classifier.
Intelsig ; Telim
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/166536

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