The problem of obtaining the maximum a posteriori (map) estimate of a discrete random field is of fundamental importance in many areas of Computer Science. In this work, we build ...
When constructing a classifier from labeled data, it is important not to assign too much weight to any single input feature, in order to increase the robustness of the classifier....
Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. In this pap...
Building software product lines (SPLs) with features is a challenging task. Many SPL implementations support features with coarse granularity ? e.g., the ability to add and wrap e...
In this paper, we explore the problems associated with exception handling from a new dimension: the human. We designed a study that evaluates (1) different perspectives of softwar...