Decision-Making and Learning - Comparing Orthogonal Methods to Majority-Voting

Abstract

A study on learning and decision-making methods was conducted by comparing an orthogonal methodology of manipulating data versus that of a majority voting procedure. The latter method has recently become popular in the literature involving applications such as pattern recognition. To evaluate the differences between the proposed methods, data from a multidimensional paradigm involving decision making and learning are analyzed. A number of basic concepts from estimation and information theory are first discussed to understand both the motivation and the underlining issues involved in conducting this study.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2001
Accession Number
ADA529964

Entities

People

  • Daniel W. Repperger

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • C4I
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Autonomous Systems
  • Brain Waves
  • Classification
  • Cognitive Workload
  • Complex Systems
  • Control Systems
  • Estimators
  • Information Theory
  • Kalman Filtering
  • Kalman Filters
  • Learning
  • Measurement
  • Optimal Estimators
  • Pattern Recognition
  • Recognition

Readers

  • Computational Modeling and Simulation
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML