New Neural Algorithms for Self-Organized Learning

Abstract

The research performed under this grant investigated three primary areas. First, collective excitation and distributed winner-take-all dynamics were investigated in laterally connected networks to further characterize the properties of biological self-organization. Self-organization was further investigated as part of the k-means clustering algorithm, where the trade-off between learning of new exemplars and global efficiency was optimized. Finally, a cross-validation technique, referred to as Generalized Prediction Error (GPE), was investigated as a means of predicting generalization error after training.

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Document Details

Document Type
Technical Report
Publication Date
Jul 31, 1991
Accession Number
ADA251771

Entities

People

  • John E. Moody

Organizations

  • Yale University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Clustering
  • Computer Science
  • Computers
  • Computing System Architectures
  • Dynamics
  • Efficiency
  • Excitation
  • Information Processing
  • Information Systems
  • Learning
  • Neural Networks
  • Numbers
  • Quasiparticles
  • Self Organizing Systems
  • Signal Processing

Readers

  • Fluid Mechanics and Fluid Dynamics.
  • Neural Network Machine Learning.