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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...
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