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Adaptive classification by variational Kalman filtering

open access: yes, 2016
We propose in this paper a probabilistic approach for adaptive inference of generalized nonlinear classification that combines the computational advantage of a parametric solution with the flexibility of sequential sampling techniques. We regard the parameters of the classifier as latent states in a first order Markov process and propose an algorithm ...
Sykacek, P, Roberts, S
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