The Cascade-Correlation Learning Architecture

Abstract

Cascade-Correlation is a new architecture and supervised learning algorithm for artificial neural networks. Instead of just adjusting the weights in a network of fixed topology, Cascade-Correlation begins with a minimal network, then automatically trains and adds new hidden units one by one, creating a multi-layer structure. Once a new hidden unit has been added to the network, its input-side weights are frozen. This unit then becomes a permanent feature-detector in the network, available for producing outputs or for creating other, more complex feature detectors. The Cascade-Correlation architecture has several advantages over existing algorithms: it learns very quickly, the network determines its own size and topology, it retains the structures it has built even if the training set changes, and it requires no back-propagation of error signals through the connections of the network.

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Document Details

Document Type
Technical Report
Publication Date
Feb 14, 1990
Accession Number
ADA256635

Entities

People

  • Christian Lebiere
  • Scott E. Fahlman

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Cognitive Science
  • Complex Systems
  • Computations
  • Computer Science
  • Detectors
  • Information Processing
  • Information Systems
  • Learning
  • Modular Construction
  • Moving Targets
  • Neural Networks
  • Recognition
  • Residuals
  • Standards
  • Targets
  • Training

Fields of Study

  • Computer science

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks