In many complex machine learning applications there is a need to learn multiple interdependent output variables, where knowledge of these interdependencies can be exploited to impr...
Domain adaptation is a fundamental learning problem where one wishes to use labeled data from one or several source domains to learn a hypothesis performing well on a different, y...
The use of Mercer kernel methods in statistical learning theory provides for strong learning capabilities, as seen in kernel principal component analysis and support vector machin...
The issue of initializing the model of a new student is of great importance for educational applications that aim at offering individualized support to students. In this paper we ...
In recent work we have developed a novel approach to the design and implementation of an online portal (ePortal) to help application engineers find replacements for electronic par...