Adaptive classification by variational Kalman filtering
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...
| Main Authors: | , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Neural information processing systems foundation
2003
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