Abstract
In the context of pattern recognition area, a small number of clustering techniques are dedicated to the on-line classification of non-stationary data. This paper presents a new algorithm designed with specific properties for the dynamical modeling of classes. This algorithm, called AUDyC (Auto-adaptive and Dynamical Clustering), is based on an unsupervised neural network with full auto-adaptive abilities. The classes modeling is obtained using Gaussian prototypes. Thanks to specific learning strategies, prototypes and classes are created, adapted or eliminated in order to incorporate new knowledge from on-line data. To do that, new learning rules have been developed into three stages: “Classification”, “Fusion” and “Evaluation”. The results show the real abilities of the AUDyC network to track classes and then to model their evolutions thanks to the adaptation of the prototypes parameters.
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Lecoeuche, S., Lurette, C. (2003). Auto-adaptive and Dynamical Clustering Neural Network. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_42
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DOI: https://doi.org/10.1007/3-540-44989-2_42
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