Performance Measures for Adaptive Decisioning Systems

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).

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Document Details

Document Type
Technical Report
Publication Date
Sep 11, 1991
Accession Number
ADA243183

Entities

People

  • Robert Y. Levine
  • Timothy S. Khuon

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Adaptive Systems
  • Air Force
  • Algorithms
  • Data Sets
  • Detection
  • Equations
  • Extrapolation
  • False Alarms
  • Hypotheses
  • Learning
  • Probability
  • Probability Distributions
  • Statistics
  • Training
  • Uncertainty
  • Warning Systems

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  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neural Network Machine Learning.
  • Psychometric Testing or Psychological Assessment.