Using the Kohonen Algorithm for Quick Initialization of Simple Competitive Learning Algorithm.
(1999) European Symposium on Artificial Neural Networks (ESANN′99) — Location: Bruges (Belgium) (21.April.1999)
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- Authors
- Abstract
- In a previous paper ([1], ESANN’97), we compared the Kohonen algorithm (SOM) to Simple Competitive Learning Algorithm (SCL) when the goal is to reconstruct an unknown density. We showed that for that purpose, the SOM algorithm quickly provides an excellent approximation of the initial density, when the frequencies of each class are taken into account to weight the quantifiers of the classes. Another important property of the SOM is the well known topology conservation, which implies that neighbor data are classified into the same class (as usual) or into neighbor classes. In this paper, we study another interesting property of the SOM algorithm, that holds for any fixed number of quantifiers. We show that even we use those approaches only for quantization, the SOM algorithm can be successfully used to accelerate in a very large proportion the speed of convergence of the classical Simple Competitive Learning Algorithm (SCL).
- Affiliations
Université Paris 1 SAMOS-MATISSE
Citations
de Bodt, E., Cottrell, M., & Verleysen, M. (1999). Using the Kohonen Algorithm for Quick Initialization of Simple Competitive Learning Algorithm. Proceedings of the European Symposium on Artificial Neural Networks (ESANN′99), p. 19-26. https://hdl.handle.net/2078.5/254031