Abstract
In the article, the influence of neighboring functions and learning rates on self-organizing maps (SOM) has been investigated. The target of a self-organizing map is data clustering and their graphical presentation. Bubble, Gaussian, and heuristic neighboring functions and four learning rates (linear, inverse-of-time, power series, and heuristics) have been analyzed here. The learning rate has been changed according to epochs and iterations. A comparative analysis has been made with three data sets: glass, wine, and zoo. The quantization error has been measured in order to estimate the SOM quality.
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Stefanovič, P., Kurasova, O. (2011). Influence of Learning Rates and Neighboring Functions on Self-Organizing Maps. In: Laaksonen, J., Honkela, T. (eds) Advances in Self-Organizing Maps. WSOM 2011. Lecture Notes in Computer Science, vol 6731. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21566-7_14
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DOI: https://doi.org/10.1007/978-3-642-21566-7_14
Publisher Name: Springer, Berlin, Heidelberg
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