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A meta-learning approach to the regularized learning—Case study: Blood glucose prediction

Authors
Journal
Neural Networks
0893-6080
Publisher
Elsevier
Volume
33
Identifiers
DOI: 10.1016/j.neunet.2012.05.004
Keywords
  • Learning Theory
  • Meta-Learning
  • Adaptive Parameter Choice
  • Kernel Choice
  • Regularization
  • Blood Glucose Prediction
Disciplines
  • Computer Science

Abstract

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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