Bayesian interpretation of kernel regularization provides a probabilistic framework for understanding regularization techniques commonly used in machine learning, particularly in the context of kernel methods. Regularization is generally employed to prevent overfitting by imposing a penalty on the complexity of the model. In Bayesian terms, this can be interpreted in terms of prior distributions on model parameters.

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