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Classification using set-valued Kalman filtering and Levi's decision theory

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2 Author(s)
Moon, T.K. ; Dept. of Electr. Eng., Utah State Univ., Logan, UT, USA ; Budge, S.E.

We consider the problem of using Levi's expected epistemic decision theory for classification when the hypotheses are of different informational values, conditioned on convex sets obtained from a set-valued Kalman filter. The background of epistemic utility decision theory with convex probabilities is outlined and a brief introduction to set-valued estimation is given. The decision theory is applied to a classifier in a multiple-target tracking scenario. A new probability density, appropriate for classification using the ratio of intensities, is introduced

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Systems, Man and Cybernetics, IEEE Transactions on  (Volume:24 ,  Issue: 2 )