TY - GEN
T1 - A self-organizing neural tree for large-set pattern classification
AU - Song, Hee Heon
AU - Lee, Seong Whan
N1 - Funding Information:
This research was supported by the Directed Ba- sic Research Fund of Korea Science and Engineering Foundation.
Publisher Copyright:
© 1995 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 1995
Y1 - 1995
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
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U2 - 10.1109/ICDAR.1995.602110
DO - 10.1109/ICDAR.1995.602110
M3 - Conference contribution
AN - SCOPUS:84935071857
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
T2 - 3rd International Conference on Document Analysis and Recognition, ICDAR 1995
Y2 - 14 August 1995 through 16 August 1995
ER -