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

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. c 2007 Elsevier Ltd. All rights reserved.

• Type: Journal
• Year: 2007
• Where: NN
• Authors: Shen Furao, Tomotaka Ogura, Osamu Hasegawa
• Keywords: Incremental Neural Network, Neural Networks, NN 2007, On-line Unsupervised Classification, Unsupervised Learning Tasks