Complexity, or in other words compactness, of models generated by rule learners is one of often neglected issues, although it has a profound effect on the success of any project t...
We describe SVMotif, a support vector machine-based learning algorithm for identification of cellular DNA transcription factor (TF) motifs extrapolated from known TF-gene interact...
Mark A. Kon, Yue Fan, Dustin T. Holloway, Charles ...
Several works have shown that the covering test in relational learning exhibits a phase transition in its covering probability. It is argued that this phase transition dooms every...
In our research we study rational agents which learn how to choose the best conditional, partial plan in any situation. The agent uses an incomplete symbolic inference engine, emp...
We introduce a polynomial-time algorithm to learn Bayesian networks whose structure is restricted to nodes with in-degree at most k and to edges consistent with the optimal branch...
Optimal Component Analysis (OCA) is a linear method for feature extraction and dimension reduction. It has been widely used in many applications such as face and object recognitio...
Our research addresses the issue of developing knowledge-based agents that capture and use the problem solving knowledge of subject matter experts from diverse application domains...