A Probabilistic Computational Framework for Neural Network Models

Abstract

Information retrieval in a 'connectionist' or neural network is viewed as computing the most probable value of the information to be retrieved with respect to a probability density function, P. With a minimal number of assumptions, the 'energy' function that a neural network minimizes during information retrieval is shown to uniquely specify P. Inspection of the form of P indicates the class of probabilistic environments that can be learned. Learning algorithms can be analyzed and designed by using maximum likelihood estimation techniques to estimate the parameters of P. The large class of nonlinear auto-associative networks analyzed by Cohen and Grossberg (1983), nonlinear associative multi-layer back-propagation networks (Rumelhart, Hinton, & Williams, 1986), and certain classes of nonlinear multi-stage networks are analyzed within the proposed computational framework. Keywords: Artificial intelligence, Connectionism, Non-linear associator.

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Document Details

Document Type
Technical Report
Publication Date
Sep 29, 1987
Accession Number
ADA218969

Entities

People

  • Richard M. Golden

Organizations

  • Carnegie Mellon University

Tags

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Availability
  • Classification
  • Computer Science
  • Computers
  • Information Retrieval
  • Learning
  • Maximum Likelihood Estimation
  • Military Research
  • Neural Networks
  • Probability Density Functions
  • Procurement
  • Psychology
  • Security
  • United States
  • United States Government
  • Universities

Readers

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

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks