A meta-learning approach to the regularized learning-case study: blood glucose prediction.
Neural Netw
; 33: 181-93, 2012 Sep.
Article
in En
| MEDLINE
| ID: mdl-22706092
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Blood Glucose
/
Diabetes Mellitus
/
Learning
Type of study:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Adolescent
/
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Language:
En
Journal:
Neural Netw
Journal subject:
NEUROLOGIA
Year:
2012
Document type:
Article
Affiliation country:
Austria