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Departments, institutes and facilities
- Fachbereich Wirtschaftswissenschaften (42)
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Document Type
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Year of publication
- 2004 (152) (remove)
Keywords
Die Fachhochschulen Bonn-Rhein-Sieg und RheinAhrCampus als Instrumente im regionalen Strukturwandel
(2004)
The Bonn region had to undergo a serious structural change because of the loss of its function as the capital of Germany. In this empirical study the role of the two newly founded universities for applied sciences Bonn-Rhein-Sieg and RheinAhrCampus in the process of regional structural change is examined. What was and still is their contribution to innovative regional development? The special focus of this study is on the number of students and graduates, the transfer of knowledge and technology and the spin-offs and start-ups.
Balanced IT-Decision Card
(2004)
IT-Kennzahlen für die Praxis
(2004)
Fasting Intact Proinsulin Is a Highly Specific Predictor of Insulin Resistance in Type 2 Diabetes
(2004)
OBJECTIVE—In later stages of type 2 diabetes, proinsulin and proinsulin-like molecules are secreted in increasing amounts with insulin. A recently introduced chemiluminescence assay is able to detect the uncleaved “intact” proinsulin and differentiate it from proinsulin-like molecules. This investigation explored the predictive value of intact proinsulin as an insulin resistance marker.
Machine learning seems to offer the solution to many problems in user modelling. However, one tends to run into similar problems each time one tries to apply out-of-the-box solutions to machine learning. This article closely relates the user modelling problem to the machine learning problem. It explicates some inherent dilemmas that are likely to be overlooked when applying machine learning algorithms in user modelling. Some examples illustrate how specific approaches deliver satisfying results and discuss underlying assumptions on the domain or how learned hypotheses relate to the requirements on the user model. Finally, some new or underestimated approaches offering promising perspectives in combined systems are discussed. The article concludes with a tentative "checklist" that one might like to consider when planning to apply machine learning to user modelling techniques.
Machine Learning seems to offer the solution to the central problem in recommender systems: Learning to recommend interesting items from observations. However, one tends to run into similar problems each time one tries to apply out-of-the-box solutions from Machine Learning. This article relates the problem of recommendation by user modeling closely to the machine learning problem and explicates some inherent dilemmas. A few examples will illustrate specific approaches and discuss underlying assumptions on the domain or how learned hypotheses relate to requirements on the user model. The article concludes with a tentative 'checklist' that one might like to consider when thinking about to use Machine Learning in User Adaptive environments such as recommender systems.