A Complexity Theory of Neural Networks

Abstract

Significant results have been obtained on the computation complexity of analog neural networks, and distribute voting. The computing power and learning algorithms for limited precision analog neural networks have been investigated. Lower bounds for constant depth, polynomial size analog neural networks, and a limited version of discrete neural networks have been obtained. The work on distributed voting has important applications for distributed computation in the presence of faults, and the management of replicated databases. Keywords: Neural networks, Complexity theory, Fault tolerance, Learning.

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Document Details

Document Type
Technical Report
Publication Date
Apr 14, 1990
Accession Number
ADA229432

Entities

People

  • Georg Schnitger
  • Ian Parberry
  • Piotr Berman

Organizations

  • Pennsylvania State University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Computations
  • Computer Programming
  • Computer Programs
  • Computer Science
  • Computers
  • Databases
  • Information Systems
  • Learning
  • Machine Learning
  • Neural Networks
  • Polynomials
  • Precision
  • Systems Science

Fields of Study

  • Computer science

Readers

  • Approximation Theory.
  • Neural Network Machine Learning.
  • Parallel and Distributed Computing.

Technology Areas

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