Classification by EM-Trained Dynamic Artificial Neural Nets Based on Hidden Perceptrons,

Abstract

We propose to classify points in R d by functions related to two-layer (a single hidden layer) feedforward artificial neural nets (ANNs). These functions, dubbed dynamic ANNs (DANNs), arise in a rather natural way from probabilistic and also statistical considerations. We treat the binary classification problem and outline an approach to the n-ary classification problem. There are two key ideas. The probabilistic idea is that DANNs are conditional probabilities certain mixture models. The statistical idea is that these models, and hence the DANNs defined by them, are conveniently trainable by an expectation - maximization (EM) algorithm.

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1992
Accession Number
ADP007152

Entities

People

  • Arthur Nadas

Organizations

  • IBM Thomas J. Watson Research Center

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Computer Science
  • Data Science
  • Engineering
  • Information Science
  • Mathematics
  • Probability
  • Statistics
  • Theoretical Computer Science

Fields of Study

  • Computer science
  • Mathematics

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design