TY - GEN
T1 - A simple genetic approach to adaptive processing of tree-structure patterns
AU - Cho, Siu Yeung
AU - Chi, Zheru
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2004
Y1 - 2004
N2 - This paper describes a learning scheme of a structural connectionist architecture based on a simple genetic approach to adaptive processing of tree-structures representation. Conventionally, one of the most popular supervised learning formulations of tree-structures processing is Backpropagation Through Structures (BPTS) [1]. The BPTS algorithm has been successfully applied to a number of learning tasks that involved complex symbolic structural patterns such as image semantic, internet behaviour, and chemical compound. However, this BPTS typed algorithm suffers from the long-term dependency problem in learning very deep tree structures. In this paper, we propose a simple genetic evolution approach for this processing. The idea of this algorithm is to tune the learning parameters by the genetic evolution with specified binary chromosome structures. Experimental results significantly support the capabilities of our proposed approach to classify and recognize structural patterns in terms of generalization capability.
AB - This paper describes a learning scheme of a structural connectionist architecture based on a simple genetic approach to adaptive processing of tree-structures representation. Conventionally, one of the most popular supervised learning formulations of tree-structures processing is Backpropagation Through Structures (BPTS) [1]. The BPTS algorithm has been successfully applied to a number of learning tasks that involved complex symbolic structural patterns such as image semantic, internet behaviour, and chemical compound. However, this BPTS typed algorithm suffers from the long-term dependency problem in learning very deep tree structures. In this paper, we propose a simple genetic evolution approach for this processing. The idea of this algorithm is to tune the learning parameters by the genetic evolution with specified binary chromosome structures. Experimental results significantly support the capabilities of our proposed approach to classify and recognize structural patterns in terms of generalization capability.
UR - http://www.scopus.com/inward/record.url?scp=12744281497&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:12744281497
SN - 1932415335
SN - 9781932415339
T3 - Proceedings of the International Conference on Artificial Intelligence, IC-AI'04
SP - 555
EP - 561
BT - Proceedings of the International Conference on Artificial Intelligence, IC-AI'04 and Proceedings of the International Conference on Machine Learning; Models, Technologies and Applications, MLMTA'04)
A2 - Arabnia, H.R.
A2 - Youngsong, M.
T2 - Proceedings of the International Conference on Artificial Intelligence, IC-AI'04
Y2 - 21 June 2004 through 24 June 2004
ER -