AuthorsV. Naumova, S. V. Pereverzyev and S. Sampath
TitleA meta-learning approach to the regularized learning—Case study: Blood glucose prediction
AfilliationScientific Computing
StatusPublished
Publication TypeJournal Article
Year of Publication2012
JournalNeural Networks
Volume33
Pagination181–193
PublisherElsevier
KeywordsLearning theory; Meta-learning; Adaptive parameter choice; Kernel choice; Regularization; Blood glucose prediction
Abstract

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.

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