Traditional knowledge representations were developed to encode complete, explicit and executable programs, a goal that makes them less than ideal for representing the incomplete an...
Attribute importance measures for supervised learning are important for improving both learning accuracy and interpretability. However, it is well-known there could be bias when th...
Structured models often achieve excellent performance but can be slow at test time. We investigate structure compilation, where we replace structure with features, which are often...
This paper presents how a new teaching method in the way that a queuing theory and systems modeling or simulation course can be done, was evaluated by the teachers and the student...
Athanasios Perdos, Alexander Chatzigeorgiou, Georg...
A learner's performance does not rely only on the representation language and on the algorithm inducing a hypothesis in this language. Also the way the induced hypothesis is ...