Reference : Constrained bayesian active learning of linear classifier
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/38164
Constrained bayesian active learning of linear classifier
English
Tsakmalis, Anestis mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Chatzinotas, Symeon mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Ottersten, Björn mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
2018
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
IEEE
Yes
International
978-1-5386-4658-8
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
15-04-2018 to 20-04-2018
Calgary
Canada
[en] In this paper, an on-line interactive method is proposed for learning a linear classifier. This problem is studied within the Active Learning (AL) framework where the learning algorithm sequentially chooses unlabelled training samples and requests their class labels from an oracle in order to learn the classifier with the least queries to the oracle possible. Additionally' a constraint is introduced into this interactive learning process which limits the percentage of the samples from one “unwanted” class under a certain threshold. An optimal AL solution is derived and implemented with a sophisticated, accurate and fast Bayesian Learning method, the Expectation Propagation (EP) and its performance is demonstrated through numerical simulations.
http://hdl.handle.net/10993/38164
10.1109/ICASSP.2018.8461409
H2020 ; 742648 - AGNOSTIC - Actively Enhanced Cognition based Framework for Design of Complex Systems

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