SUPERVISED LEARNING RECURSIVE FILTERS FOR OPTIMAL STRUCTURE AND PARAMETER ADAPTIVE PATTERN RECOGNITION. CASE I: CONTINUOUS DATA.

Abstract

Recursive filters for supervised learning Bayes-optimal adaptive pattern recognition with continuous data are derived. Both off-line (or prior to actual operation) and on-line (while in operation) supervised learning is considered. The concept of structure adaptation is introduced and both structure as well as parameter adaptive optimal pattern recognition systems are obtained. Specifically, for the class of supervised learning pattern recognition problems with gaussian process models and linear dynamics, the adaptive pattern recognition systems are shown to be decomposable ('partition theorem') into a linear, non-adaptive part consisting of recursive, matched Kalman filters, a nonlinear part--a set of probability computers--that incorporates the adaptive nature of the system, and finally a linear part of the correlator-estimator (Kailath) form. (Author)

Document Details

Document Type
Technical Report
Publication Date
Sep 10, 1969
Accession Number
AD0699220

Entities

People

  • D. G. Lainiotis

Organizations

  • University of Texas at Austin

Tags

DTIC Thesaurus Topics

  • Estimators
  • Filters
  • Gaussian Processes
  • Kalman Filters
  • Learning
  • Pattern Recognition
  • Probability
  • Recognition
  • Recursive Filters
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neural Network Machine Learning.

Technology Areas

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