@inproceedings{772eab96cad94fd08f967a917ef99392,
title = "A neural tree with partial incremental learning capability",
abstract = "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.",
keywords = "Decision tree, Incremental learning, Neural networks, Neural tree, Pattern classification",
author = "Su, {Mu Chun} and Lo, {Hsu Hsun}",
year = "2007",
doi = "10.1109/ICMLC.2007.4370106",
language = "???core.languages.en_GB???",
isbn = "142440973X",
series = "Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007",
pages = "6--11",
booktitle = "Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007",
note = "6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 ; Conference date: 19-08-2007 Through 22-08-2007",
}