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Proceedings Paper

Training-set-based performance measures for data-adaptive decisioning systems
Author(s): Robert Y. Levine; Timothy S. Khuon
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Paper Abstract

Performance measures are derived for data-adaptive hypothesis testing by systems trained on stochastic data. The measures consist of the averaged performance of the system over the ensemble of training sets. The training set-based measures are contrasted with maximum aposteriori probability (MAP) test measures. It is shown that the training set-based and MAP test probabilities are equal if the training set is proportioned according to the prior probabilities of the hypotheses. Applications of training set-based measures are suggested for neural net and training set design.

Paper Details

Date Published: 16 December 1992
PDF: 11 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130857
Show Author Affiliations
Robert Y. Levine, Lincoln Lab./MIT (United States)
Timothy S. Khuon, Lincoln Lab./MIT (United States)


Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)

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