Abstract
Previously, an unbiased estimator of the generalization error called the subspace information criterion (SIC) was proposed for a finite dimensional reproducing kernel Hilbert space (RKHS). In this paper, we extend SIC so that it can be applied to any RKHSs including infinite dimensional ones. Computer simulations show that the extended SIC works well in ridge parameter selection. © Springer-Verlag Berlin Heidelberg 2002.
Cite
CITATION STYLE
Sugiyama, M., & Müller, K. R. (2002). Selecting ridge parameters in infinite dimensional hypothesis spaces. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 528–534). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_86
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