A NEURAL TREE WITH PARTIAL INCREMENTAL LEARNING CAPABILITY
This paper presents a new approach to constructing a neural tree with partial incremental learning capability.The proposed neural tree, called a quadratic-neuron-based neural tree (QUANT), is a tree structured neural network composed of neurons with quadratic neural-type junctions for pattern classification.The proposed QUANT integrates the advantages of decision trees and neural networks.Via a batch-mode training algorithm, the QUANT grows a neural tree containing quadratic neurons in its nodes.These quadratic neurons recursively partition the feature space into hyper-ellipsoidal-shaped sub-regions.The QUANT has the partial incremental capability so that it does not need to re-construct a new neural tree to accommodate new training data whenever new data are introduced to a trained QUANT.To demonstrate the performance of the proposed QUANT, several pattern recognition problems were tested.
Neural networks Neural tree Decision tree Incremental learning Pattern classification
MU-CHUN SU HSU-HSUN LO
Department of Computer Science & Information Engineering, National Central University, Taiwan, R.O.C.
国际会议
2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)
香港
英文
6-11
2007-08-19(万方平台首次上网日期,不代表论文的发表时间)