In multiple criteria Markov Decision Processes (MDP) where multiple costs are incurred at every decision point, current methods solve them by minimising the expected primary cost ...
In this paper, we present a unified knowledge based approach for sense disambiguation and semantic role labeling. Our approach performs both tasks through a single algorithm that ...
Learning application-specific distance metrics from labeled data is critical for both statistical classification and information retrieval. Most of the earlier work in this area h...
Geometrical symmetries are commonly exploited to improve the efficiency of search algorithms. We introduce a new logical symmetry in permutation state spaces which we call duality...
Uzi Zahavi, Ariel Felner, Robert Holte, Jonathan S...
The Web has become the largest information repository over the world. Therefore, effectively and efficiently searching the Web becomes a key challenge. Previous research on Web se...
One of the well known risks of large margin training methods, such as boosting and support vector machines (SVMs), is their sensitivity to outliers. These risks are normally mitig...
The expected first hitting time is an important issue in theoretical analyses of evolutionary algorithms since it implies the average computational time complexity. In this paper,...
straction is a useful tool for agents interacting with environments. Good state abstractions are compact, reuseable, and easy to learn from sample data. This paper and extends two...