Selecting Ridge Parameters in Infinite Dimensional Hypothesis Spaces

Authors:
Masashi Sugiyama;Klaus-Robert Müller
Affiliations:
-;-
Venue:
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Year:
2002

Citing 2
Cited 0

Subspace Information Criterion for Model Selection

Neural Computation
Subspace information criterion for nonquadratic regularizers-Model selection for sparse regressors

IEEE Transactions on Neural Networks

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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.