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Abstract

The paper presents Kernel Ridge Regression, a nonlinear extension of the well known statistical model of ridge regression. New insights on the method are also presented. In particular, the connection between ridge regression and local translation-invariant squared loss minimization algorithm is shown. An iterative training algorithm is proposed, that allows training the KRR for large datasets. The training time is empirically found to scale quadratically with the number of samples. The application of the model is illustrated on the real datasets.

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