Abstract
Fuzzy Labeled Self-Organizing Map is a semisupervised learning that allows the prototype vectors to be updated taking into account information related to the clusters of the data set. In this paper, this algorithm is extended to update individually the kernel radii according to Van Hulle’s approach. A significant reduction of the mean quantization error of the numerical prototype vectors is expected.
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González, I.M., García, H.L. (2007). Fuzzy Labeled Self-organizing Map with Kernel-Based Topographic Map Formation. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_35
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DOI: https://doi.org/10.1007/978-3-540-74695-9_35
Publisher Name: Springer, Berlin, Heidelberg
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