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Sequential Bayesian Estimation for Adaptive Classification

Abstract:
This paper proposes a robust algorithm to adapt a model for EEG signal classification using a modified Extended Kalman Filter (EKF). By applying Bayesian conjugate priors and marginalising the parameters, we can avoid the needs to estimate the covariances of the observation and hidden state noises. In addition, Laplace approximation is employed in our model to approximate non-Gaussian distributions as Gaussians. ©2008 IEEE.
Publication status:
Published

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Publisher copy:
10.1109/MFI.2008.4648010

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
Pages:
33-37
Host title:
2008 IEEE INTERNATIONAL CONFERENCE ON MULTISENSOR FUSION AND INTEGRATION FOR INTELLIGENT SYSTEMS, VOLS 1 AND 2
Publication date:
2008-01-01
DOI:
Source identifiers:
63747
ISBN:
9781424421435
Keywords:
Pubs id:
pubs:63747
UUID:
uuid:0620114d-e352-44b7-bd7e-e77d7bca7a6d
Local pid:
pubs:63747
Deposit date:
2012-12-19

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