HELPR: Hybrid Evolutionary Learning for Pattern Recognition

Abstract

The availability of inexpensive sensors coupled with the rise of the internet has led to a rapid expansion in the amount of data available for analysis. Although there are a myriad of uses for this data, one of the most common applications is pattern recognition. The traditional approach to creating pattern recognition systems is human intensive requiring experts with training in pattern recognition to collaborate with experts who have knowledge of the problem domain to develop a custom recognition system for a specific problem. In contrast, the HELPR software architecture has focused on the design and implementation of software tools and techniques that use evolutionary computation to synthesize target systems from raw data, thereby reducing the personnel requirements and time needed to deploy new recognition systems. Result produce by ATR systems evolved using HELPR are reported for a variety of tasks involving HRR, SAR, E3D, and image processing.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2005
Accession Number
ADA446893

Entities

People

  • Louis A. Tamburino
  • Mateen M. Rizki

Organizations

  • Wright State University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Computer Programming
  • Computer Science
  • Computers
  • Databases
  • Detection
  • Detectors
  • Feature Extraction
  • Identification
  • Image Processing
  • Machine Learning
  • Pattern Recognition
  • Recognition
  • Target Recognition
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML