Neural Networks for Interactive Image and Signal Exploitation.

Abstract

Efficient reliable neural network design methods based on sound principles were developed. The main effort was concentrated on exploring the nature of error surfaces attempting to answer the question of how many minima actually exists in any given design. To this end the following experimental approach was taken: Define a scaleable model problem with a known solution, Perform comprehensive, careful training experiments, Refine training solutions to highly accurate minima, Post process and count local minima, and Derive quantitative and qualitative measures that describe error surfaces. The Principal results show: How the number of minima varies as a function of the ration of training to network size, the probability that training from a random start converges to a minimum, and How the number of minima increase with network complexity.

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Document Details

Document Type
Technical Report
Publication Date
May 29, 1995
Accession Number
ADA299479

Entities

People

  • John Pearson

Organizations

  • Sarnoff Corporation

Tags

Communities of Interest

  • C4I
  • Human Systems
  • Space

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Computer Vision
  • Computers
  • Data Mining
  • Data Science
  • Databases
  • Health Services
  • Image Processing
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • Mathematical Analysis
  • Medical Personnel
  • Network Science
  • Neural Networks
  • Physicians

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Statistical inference.
  • Systems Analysis and Design

Technology Areas

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