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A Relationship between Generalization Error and Training Samples in Kernel Regressors

10 years 4 months ago
A Relationship between Generalization Error and Training Samples in Kernel Regressors
A relationship between generalization error and training samples in kernel regressors is discussed in this paper. The generalization error can be decomposed into two components. One is a distance between an unknown true function and an adopted model space. The other is a distance between an estimated function and the orthogonal projection of the unknown true function onto the model space. In our previous work, we gave a framework to evaluate the first component. In this paper, we theoretically analyze the second one and show that a larger set of training samples usually causes a larger generalization error. Keywords-kernel regressor; reproducing kernel Hilbert space; generalization error; sample points;
Akira Tanaka, Hideyuki Imai, Mineichi Kudo, Masaak
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2010
Where ICPR
Authors Akira Tanaka, Hideyuki Imai, Mineichi Kudo, Masaaki Miyakoshi
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