title: An enhanced self-organizing incremental neural network for online unsupervised learning.
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.
authors.affiliation: The State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, PR China. frshen@nju.edu.cn
authors.forename: Shen
authors.initials: S
authors.lastname: Furao
authors.forename: Tomotaka
authors.initials: T
authors.lastname: Ogura
authors.forename: Osamu
authors.initials: O
authors.lastname: Hasegawa
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D032701:Biomimetics; D016000:Cluster Analysis; D003198:Computer Simulation; D003491:Cybernetics; D005246:Feedback; D007091:Image Processing, Computer-Assisted; D016247:Information Storage and Retrieval; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted