An enhanced self-organizing incremental neural network for online unsupervised learning
An enhanced self-organizing incremental neural network for online unsupervised learning
复制标题
用于在线无监督学习的增强型自组织增量神经网络
DOI：
10.1016/j.neunet.2007.07.008
复制
发表时间：
2007-10-01
期刊：
影响因子：
7.8
通讯作者：
Hasegawa, Osamu
中科院分区：
文献类型：
--
作者：
Furao, Shen;Ogura, Tomotaka;Hasegawa, Osamu
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。它改进了自组织增量神经网络（SOINN）[Shen，R，Hasegawa，O.（2006年a）。一种用于在线无监督分类和拓扑学习的增量网络。Neural Networks，19，90-106]在以下方面：（1）它采用单层网络来代替SOINN的两层网络结构;（2）它分离具有高密度重叠的簇;（3）它使用比SOINN少的参数;（4）它比SOINN更稳定。人工数据集和真实数据集的实验也表明，ESOINN比SOINN更好。(C)2007爱思唯尔有限公司保留所有权利。
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, R, 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. (C) 2007 Elsevier Ltd. All rights reserved.