Abstract
We generalize the traditional supervised self-organizing map to supervised kernel self-organizing map by incorporating the kernel function to further improve its capability of solving non-linear problems. The kernel function maps the low-dimensional input space to high-dimensional feature space thus potentially makes the complex non-linear structure in the input space much easier in the mapped feature space. Qualitative and quantitative analysis of the experimental results on the two benchmark datasets illustrate the effectiveness of the proposed method.
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Yu, D., Hu, J., Song, X., Qi, Y., Tang, Z. (2013). Supervised Kernel Self-Organizing Map. In: Yang, J., Fang, F., Sun, C. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2012. Lecture Notes in Computer Science, vol 7751. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36669-7_31
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DOI: https://doi.org/10.1007/978-3-642-36669-7_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36668-0
Online ISBN: 978-3-642-36669-7
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