A Technique for the Numerical Solution of Certain Integral Equations of the First Kind
Journal of the ACM (JACM)
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
On Learning Vector-Valued Functions
Neural Computation
On regularization algorithms in learning theory
Journal of Complexity
Optimal Rates for the Regularized Least-Squares Algorithm
Foundations of Computational Mathematics
Kernel-Based Inductive Transfer
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Approximate Minimization of the Regularized Expected Error over Kernel Models
Mathematics of Operations Research
Reproducing Kernel Banach Spaces for Machine Learning
The Journal of Machine Learning Research
Universal glucose models for predicting subcutaneous glucose concentration in humans
IEEE Transactions on Information Technology in Biomedicine
Metalearning: Applications to Data Mining
Metalearning: Applications to Data Mining
Adaptive Kernel Methods Using the Balancing Principle
Foundations of Computational Mathematics
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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.