A Soft-Competitive Splitting Rule for Adaptive Tree-Structured Neural Networks

Abstract

An algorithm for generating tree structured neural networks using a soft-competitive recursive partitioning rule is described. It is demonstrated that the algorithm grows robust, honest estimators. Preliminary results on a 10 class, 240 dimensional OCR classification task are presented which show that the tree out-performs backpropagation. Arguments are made which suggest why this should be the case. The connection of the soft-competitive splitting rule to the twoing rule is described.... Soft-competition, CART, Neural networks

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Document Details

Document Type
Technical Report
Publication Date
May 17, 1993
Accession Number
ADA264936

Entities

People

  • Michael P. Perrone

Organizations

  • Brown University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Character Recognition
  • Classification
  • Cognitive Science
  • Competition
  • Computations
  • Estimators
  • Identification
  • Learning
  • Military Research
  • Neural Networks
  • Neurobehavioral Manifestations
  • Pattern Recognition
  • Probability Distributions
  • Recognition
  • Splitting
  • Training

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.

Technology Areas

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