Abstract
In recent years, kernel methods are used in many applications, such as text classification and gene recognition. The parameters of kernels are empirically decided by the context of application. In order to select the appropriate kernel parameters, kernel polarization is presented as a universal kernel optimality criterion, which is independent of the classifier to be used. However, kernel polarization has several disadvantages, leading to the inconvenience of applying such method. In this paper, a clustering algorithm called Cooperative Clustering is integrated with kernel polarization. The experimental results showed the effectiveness of the approach.
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Cao, W., Yin, C., Mu, S., Tian, S. (2013). Kernel Polarization Based on Cooperative Clustering. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_29
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DOI: https://doi.org/10.1007/978-3-642-42042-9_29
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