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NN
2006
Springer

An incremental network for on-line unsupervised classification and topology learning

13 years 2 months ago
An incremental network for on-line unsupervised classification and topology learning
This paper presents an on-line unsupervised learning mechanism for unlabeled data that are polluted by noise. Using a similarity thresholdbased and a local error-based insertion criterion, the system is able to grow incrementally and to accommodate input patterns of on-line nonstationary data distribution. A definition of a utility parameter, the error-radius, allows this system to learn the number of nodes needed to solve a task. The use of a new technique for removing nodes in low probability density regions can separate clusters with low-density overlaps and dynamically eliminate noise in the input data. The design of two-layer neural network enables this system to represent the topological structure of unsupervised on-line data, report the reasonable number of clusters, and give typical prototype patterns of every cluster without prior conditions such as a suitable number of nodes or a good initial codebook. q 2005 Elsevier Ltd. All rights reserved.
Shen Furao, Osamu Hasegawa
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where NN
Authors Shen Furao, Osamu Hasegawa
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