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Adaptive classification by variational Kalman filtering

Abstract:

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 which can be regarded as variational generalization of standard Kalman filtering. The variational Kalman filter is based on two novel lower boun...

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Publisher:
Neural information processing systems foundation
Journal:
Advances in Neural Information Processing Systems
Publication date:
2003-01-01
ISSN:
1049-5258
Source identifiers:
318940
Language:
English
Pubs id:
pubs:318940
UUID:
uuid:4431e655-f392-4c45-8f21-05d075157e5d
Local pid:
pubs:318940
Deposit date:
2014-05-09

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