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一种用于在线无监督学习的增强型自组织增量神经网络。

An enhanced self-organizing incremental neural network for online unsupervised learning.

作者信息

Furao Shen, Ogura Tomotaka, Hasegawa Osamu

机构信息

The State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, PR China.

出版信息

Neural Netw. 2007 Oct;20(8):893-903. doi: 10.1016/j.neunet.2007.07.008. Epub 2007 Aug 14.

Abstract

An enhanced self-organizing incremental neural network (ESOINN) is proposed to accomplish online unsupervised learning tasks. It improves the self-organizing incremental neural network (SOINN) [Shen, F., Hasegawa, O. (2006a). An incremental network for on-line unsupervised classification and topology learning. Neural Networks, 19, 90-106] in the following respects: (1) it adopts a single-layer network to take the place of the two-layer network structure of SOINN; (2) it separates clusters with high-density overlap; (3) it uses fewer parameters than SOINN; and (4) it is more stable than SOINN. The experiments for both the artificial dataset and the real-world dataset also show that ESOINN works better than SOINN.

摘要

提出了一种增强型自组织增量神经网络(ESOINN)来完成在线无监督学习任务。它在以下方面改进了自组织增量神经网络(SOINN)[Shen, F., Hasegawa, O. (2006a). An incremental network for on-line unsupervised classification and topology learning. Neural Networks, 19, 90 - 106]:(1)采用单层网络代替SOINN的双层网络结构;(2)分离高密度重叠的聚类;(3)使用比SOINN更少的参数;(4)比SOINN更稳定。人工数据集和真实世界数据集的实验也表明ESOINN比SOINN表现更好。

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