Abstract
To determine the optimal configuration of the kernel parameters, the physical characteristic of the mapped least squares (LS) support vector machine (SVM) is investigated by analyzing the frequency responses of the filters deduced from the support value and LS-SVM itself. This analysis of the mapped LS-SVM with Gaussian kernel illustrates that the optimal configuration of the kernel parameter exists and the regulation constant is directly determined by the frequency content of the image. The image regression estimation experiments demonstrate the effectiveness of the presented method.
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ZHENG, S., LIU, J., Tian, Jw. (2006). Research of Mapped Least Squares SVM Optimal Configuration. In: Abraham, A., de Baets, B., Köppen, M., Nickolay, B. (eds) Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31662-0_52
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DOI: https://doi.org/10.1007/3-540-31662-0_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31649-7
Online ISBN: 978-3-540-31662-6
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