Accession Number:

ADA243183

Title:

Performance Measures for Adaptive Decisioning Systems

Descriptive Note:

Technical rept.,

Corporate Author:

MASSACHUSETTS INST OF TECH LEXINGTON LINCOLN LAB

Personal Author(s):

Report Date:

1991-09-11

Pagination or Media Count:

27.0

Abstract:

Performance measures are derived for data-adaptive hypothesis testing by systems trained on stochastic data. The measures consist of the average performance of the systems over an ensemble of training sets. The uncertainties derivable from training sets represents an irreducible uncertainty inherent in the learning procedure. Data-adaptive system estimates are contrasted with classical hypothesis testing, in which optimum tests are based on an assumed data model. In addition, a performance estimate for the maximum a posteriori probability MAP N-hypothesis test is derived based on a neural-net formulation of the test. The performance of adaptive systems on a binary test of uniformly distributed data is compared with the data-adaptive and MAP estimates. The adaptive systems considered are linear extrapolation from data LINEXT and a back-propagation neural net BPNN.

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE