In this paper we present a flexible assessment framework for eLearning based on semantic web technologies. The assessment techniques are formalized as reasoning rules. These rules...
With the growing use of distributed information networks, there is an increasing need for algorithmic and system solutions for data-driven knowledge acquisition using distributed,...
Doina Caragea, Jaime Reinoso, Adrian Silvescu, Vas...
Abstract We present a new margin-based approach to first-order rule learning. The approach addresses many of the prominent challenges in first-order rule learning, such as the comp...
In this paper we present a variational Bayes (VB) framework for learning continuous hidden Markov models (CHMMs), and we examine the VB framework within active learning. Unlike a ...
Discovering rare categories and classifying new instances of them is
an important data mining issue in many fields, but fully supervised
learning of a rare class classifier is pr...