A self-organizing neural tree for large-set pattern classification

Hee Heon Song, Seong Whan Lee

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

5 Citations (Scopus)

Abstract

Neural networks have been successfully applied to various pattern classification problems owing to their learning ability, high discrimination power, and excellent generalization ability. However, for the case of classifying patterns which are large-set and require complex decision boundaries in high-dimensional pattern space, the greater part of conventional neural networks suffer from some of difficult problems to solve, such as the structure and size of the network, the computational complexity, and so on. In this paper, to cope with these difficulties, we propose a new self-organizing neural tree and its learning algorithm. The basic idea is to partition pattern space hierarchically using the tree-structured network composed of subnetworks with topology-preserving mapping ability.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995
PublisherIEEE Computer Society
Pages1111-1114
Number of pages4
ISBN (Electronic)0818671289
DOIs
Publication statusPublished - 1995 Jan 1
Event3rd International Conference on Document Analysis and Recognition, ICDAR 1995 - Montreal, Canada
Duration: 1995 Aug 141995 Aug 16

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Volume2
ISSN (Print)1520-5363

Conference

Conference3rd International Conference on Document Analysis and Recognition, ICDAR 1995
CountryCanada
CityMontreal
Period95/8/1495/8/16

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Song, H. H., & Lee, S. W. (1995). A self-organizing neural tree for large-set pattern classification. In Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995 (pp. 1111-1114). [602110] (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR; Vol. 2). IEEE Computer Society. https://doi.org/10.1109/ICDAR.1995.602110