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Theoretical validity of empirical error minimization in multiple kernel regressors is discussed in this paper. Generalization error of a kernel machine is usually evaluated by the induced norm of the difference between an unknown true function and an estimated one in an appropriate reproducing kernel Hilbert space. It is well known that empirical error minimization also achieves the minimum generalization...
Learning based on kernel machines is widely known as a powerful tool for various fields of information science including signal processing such as function estimation from finite sampling points. One of central topics of kernel machines is model selection, especially selection of a kernel or its parameters. In our previous works, we investigated the generalization error of a model space itself corresponding...
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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