Cresceptron: A Self-Organizing Neural Network Which Grows Adaptively

John Weng, Narendra Ahuja, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Cresceptron represents a new approach to neural networks. It uses a hierarchical framework to grow neural networks automatically, adaptively and incrementally through learning. At every level of the hierarchy, new concepts are detected automatically and the network grows by creating new neurons and synapses which memorize the new concepts and their context. The training samples are generalized to other perceptually equivalent items through hierarchical tolerance of deviation. The neural network recognizes the learned items and their variations by hierarchically associating the learned knowledge with the input. It segments the recognized items from the input through back tracking along the response paths.

Original languageEnglish (US)
Title of host publicationProceedings - 1992 International Joint Conference on Neural Networks, IJCNN 1992
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages576-581
Number of pages6
ISBN (Electronic)0780305590
DOIs
StatePublished - 1992
Event1992 International Joint Conference on Neural Networks, IJCNN 1992 - Baltimore, United States
Duration: Jun 7 1992Jun 11 1992

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume1

Conference

Conference1992 International Joint Conference on Neural Networks, IJCNN 1992
Country/TerritoryUnited States
CityBaltimore
Period6/7/926/11/92

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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