A meta-learning approach to the regularized learning-case 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.
Johann Radon Institute for Computational and Applied Mathematics-RICAM, Austrian Academy of Sciences, A-4040 Linz, Austria. valeriya.naumova@oeaw.ac.at
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Naumova
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Pereverzyev
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Sivananthan
D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001786:Blood Glucose / Q000378:metabolism*; D016022:Case-Control Studies; D003920:Diabetes Mellitus / Q000097:blood* / Q000175:diagnosis*; D005260:Female; D006801:Humans; D007858:Learning / Q000502:physiology; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D055815:Young Adult
D001786:Blood Glucose