A neural tree with partial incremental learning capability

Mu Chun Su, Hsu Hsun Lo

研究成果: 書貢獻/報告類型會議論文篇章同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文???core.languages.en_GB???
主出版物標題Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
頁面6-11
頁數6
DOIs
出版狀態已出版 - 2007
事件6th International Conference on Machine Learning and Cybernetics, ICMLC 2007 - Hong Kong, China
持續時間: 19 8月 200722 8月 2007

出版系列

名字Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, ICMLC 2007
1

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???event.eventtypes.event.conference???6th International Conference on Machine Learning and Cybernetics, ICMLC 2007
國家/地區China
城市Hong Kong
期間19/08/0722/08/07

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