Abstract
In this paper, a ridgelet kernel regression model is proposed for approximation of multivariate functions, especially those with certain kinds of spatial inhomogeneities. It is based on ridgelet theory, kernel and regularization technology from which we can deduce a regularized kernel regression form. Using the objective function solved by quadratic programming to define a fitness function, we adopt particle swarm optimization algorithm to optimize the directions of ridgelets. Theoretical analysis proves the superiority of ridgelet kernel regression for multivariate functions. Experiments in regression indicate that it not only outperforms support vector machine for a wide range of multivariate functions, but also is robust and quite competitive on training of time.
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Yang, S., Wang, M., Jiao, L., Li, Q. (2005). A Novel Ridgelet Kernel Regression Method. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_143
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DOI: https://doi.org/10.1007/11427391_143
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
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