= Bayesian interpretation of kernel regularization
{wiki=Bayesian_interpretation_of_kernel_regularization}
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.
Back to article page