Nonparametric Function Estimation and Visualization with Applications to C2

Abstract

This project focused on the development of fast, accurate density estimation procedures. The methods raised basic research issues as to the implementation, computational complexity, visualization, and optimization of estimators in this class. In addition to being useful in a direct role, it is argued that density estimation plays a crucial role in clustering algorithms, discriminant methods and pattern recognition. All of these methods are used extensively in Situation and Informational Awareness and Understanding and in Monitoring and Discovery Processes. In addition, because of their intuitive appeal and ease in understanding, visually rendered density and function estimators provide a natural format for human-computer interactions with decision makers. This report describes results related implementation, computational complexity, visualization, optimization and application of recursive orthonormal density estimators.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2002
Accession Number
ADA416423

Entities

People

  • Edward Wegman

Organizations

  • George Mason University

Tags

Communities of Interest

  • C4I
  • Cyber
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Cognitive Systems Engineering
  • Computational Complexity
  • Computational Science
  • Computer Science
  • Computers
  • Data Analysis
  • Data Mining
  • Data Visualization
  • Estimators
  • Human-Computer Interaction
  • Information Science
  • Parallel Computing
  • Statistical Analysis
  • Statistical Data
  • Virtual Reality
  • Visualizations

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Neural Network Machine Learning.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms