Abstract
A method for generating a self-organizing map of line images is proposed. In the proposed method, called the NG×SOM, a set of data distributions is represented by a product space organized by a set of neural gas networks (NGs) and a self-organizing map (SOM). In this paper, it is assumed that the line images dealt with by the NG×SOM have the same, yet unknown, topology. Thus the task of the NG×SOM is to generate a map of line images with the same topology, in which the images are continuously and naturally morphed from one into another. We applied the NG×SOM to a handwritten character recognition task. The results obtained show that this method is effective, particularly when the number of training data is small.
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Otani, M., Gunya, K., Furukawa, T. (2009). Line Image Classification by NG×SOM: Application to Handwritten Character Recognition. In: Príncipe, J.C., Miikkulainen, R. (eds) Advances in Self-Organizing Maps. WSOM 2009. Lecture Notes in Computer Science, vol 5629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02397-2_25
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DOI: https://doi.org/10.1007/978-3-642-02397-2_25
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