A meta-learning approach to the regularized learningCase study: Blood glucose prediction
In this paper we present a new scheme of a kernel-based regularization learning algorithm, in which the kernel and the regularization parameter are adaptively chosen on the base of previous experience with similar learning tasks. The construction of such a scheme is motivated by the problem of prediction of the blood glucose levels of diabetic patients. We describe how the proposed scheme can be used for this problem and report the results of the tests with real clinical data as well as comparing them with existing literature.
meta-learning regularization blood glucose prediction learning theory adaptive parameter choice kernel choice