A Self-Organizing Neural Structure for Concept Formation from Incomplete Observation

Noriyasu Homma, Madan M. Gupta

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

We propose a self-organizing neural structure with dynamic and spatial changing weights for a feature space representation of concept formation. An essential core of this self-organization is based on an unsupervised learning with incomplete information for the dynamic changing and an extended Hebbian rule for the spatial changing. A concept formation problem requires the neural network to acquire the complete feature space structure of a concept information using an incomplete observation of the concept. The connection structure of self-organizing network can store with the information structure by using the two rules. The Hebbian rule can create a necessary connection corresponding to a feature space substructure of the complete information. On the other hand, unsupervised learning can delete unnecessary connections. Finally concept formation ability of the proposed neural network is proven under some conditions.

Original languageEnglish
Pages2615-2618
Number of pages4
Publication statusPublished - 2003
EventInternational Joint Conference on Neural Networks 2003 - Portland, OR, United States
Duration: 2003 Jul 202003 Jul 24

Other

OtherInternational Joint Conference on Neural Networks 2003
CountryUnited States
CityPortland, OR
Period03/7/2003/7/24

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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    Homma, N., & Gupta, M. M. (2003). A Self-Organizing Neural Structure for Concept Formation from Incomplete Observation. 2615-2618. Paper presented at International Joint Conference on Neural Networks 2003, Portland, OR, United States.