PATTERN RECOGNITION OF STOCHASTIC PROCESSES.

Abstract

The class of differential processes are considered. For their class of processes, it is shown that there is an advantage to transforming the observed process x(t) into an infinite vector. Having derived the properties of the vector, it is then shown that the Decision Theory solution to the K-category recognition problems can be formulated in terms of the components of this vector. An example of signal detection is then worked out. It yields the optimum detector when the signal is a saw-tooth function. Finally, the K-category solution is explicitly derived.

Document Details

Document Type
Technical Report
Publication Date
Jul 07, 1966
Accession Number
AD0489190

Entities

People

  • Joel Owen

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Change Detection
  • Decision Theory
  • Detection
  • Detectors
  • Pattern Recognition
  • Recognition
  • Signal Detection
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

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