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 | |
| Publication status | Published - 1995 Jan 1 |
| 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 | Canada |
| City | Montreal |
| Period | 95/8/14 → 95/8/16 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
Cite this
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
}
TY - GEN
T1 - A self-organizing neural tree for large-set pattern classification
AU - Song, Hee Heon
AU - Lee, Seong Whan
PY - 1995/1/1
Y1 - 1995/1/1
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=84935071857&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84935071857&partnerID=8YFLogxK
U2 - 10.1109/ICDAR.1995.602110
DO - 10.1109/ICDAR.1995.602110
M3 - Conference contribution
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 1111
EP - 1114
BT - Proceedings of the 3rd International Conference on Document Analysis and Recognition, ICDAR 1995
PB - IEEE Computer Society
ER -