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

Hee Heon Song; Seong Whan Lee

doi:10.1109/ICDAR.1995.602110

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

Hee Heon Song, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995
Publisher	IEEE Computer Society
Pages	1111-1114
Number of pages	4
ISBN (Electronic)	0818671289
DOIs	https://doi.org/10.1109/ICDAR.1995.602110
Publication status	Published - 1995
Event	3rd International Conference on Document Analysis and Recognition, ICDAR 1995 - Montreal, Canada Duration: 1995 Aug 14 → 1995 Aug 16

Publication series

Name	Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
Volume	2
ISSN (Print)	1520-5363

Conference

Conference	3rd International Conference on Document Analysis and Recognition, ICDAR 1995
Country/Territory	Canada
City	Montreal
Period	95/8/14 → 95/8/16

Bibliographical note

Funding Information:
This research was supported by the Directed Ba- sic Research Fund of Korea Science and Engineering Foundation.

Publisher Copyright:
© 1995 IEEE.

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

Access to Document

10.1109/ICDAR.1995.602110

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). Article 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

A self-organizing neural tree for large-set pattern classification. / Song, Hee Heon; Lee, Seong Whan.
Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995. IEEE Computer Society, 1995. p. 1111-1114 602110 (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Song, HH & Lee, SW 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., 602110, Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, vol. 2, IEEE Computer Society, pp. 1111-1114, 3rd International Conference on Document Analysis and Recognition, ICDAR 1995, Montreal, Canada, 95/8/14. https://doi.org/10.1109/ICDAR.1995.602110

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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.",

author = "Song, {Hee Heon} and Lee, {Seong Whan}",

note = "Funding Information: This research was supported by the Directed Ba- sic Research Fund of Korea Science and Engineering Foundation. Publisher Copyright: {\textcopyright} 1995 IEEE.; 3rd International Conference on Document Analysis and Recognition, ICDAR 1995 ; Conference date: 14-08-1995 Through 16-08-1995",

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AB - 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.

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

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

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Cite this