Document Type

Article

Journal/Book Title/Conference

IEEE Transactions on Systems, Man, and Cybernetics

Volume

24

Issue

2

Publisher

IEEE

Publication Date

2-1994

First Page

313

Last Page

319

DOI

10.1109/21.281429

Abstract

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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