Unsupervised Splitting Rules for Neural Tree Classifiers

Abstract

This paper presents two unsupervised neural network splitting rules for use with CART-like neural tree algorithms in high dimensional data space. These splitting rules use an adaptive variance estimate to avoid some possible local minima which arise in unsupervised methods. We explain when the unsupervised splitting rules outperform supervised neural network splitting rules and when the unsupervised splitting rules outperform the standard node impurity splitting rules of CART. Using these unsupervised splitting rules lead to a nonparametric classifier for high dimensional space that extracts local features in an optimized way.... CART, Unsupervised feature extraction, Neural trees.

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Document Details

Document Type
Technical Report
Publication Date
May 17, 1993
Accession Number
ADA264961

Entities

People

  • Michael P. Perone
  • Nathan Intrator

Organizations

  • Brown University

Tags

Communities of Interest

  • Counter IED
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Computer Science
  • Computers
  • Data Analysis
  • Extraction
  • Feature Extraction
  • Gaussian Distributions
  • Identification
  • Information Science
  • Machine Learning
  • Military Research
  • Neural Networks
  • Recognition
  • Security
  • Training
  • Universities

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Quantum spin resonance or Electron Paramagnetic Resonance spectroscopy.

Technology Areas

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