—Nowadays, embedded systems are widely used. It is extremely difficult to analyze safety issues in embedded systems, to relate the safety analysis results to the actual parts, a...
Yasmin I. Al-Zokari, Daniel Schneider, Dirk Zeckze...
Using relevance feedback can significantly improve (ad hoc) retrieval effectiveness. Yet, if little feedback is available, effectively exploiting it is a challenge. To that end,...
We present a novel approach to fusing document lists that are retrieved in response to a query. Our approach is based on utilizing information induced from inter-document similarit...
Clustering for better representation of the diversity of text or image search results has been studied extensively. In this paper, we extend this methodology to the novel domain o...
While empirical evaluations are a common research method in some areas of Artificial Intelligence (AI), others still neglect this approach. This article outlines both the opportun...
In this paper, we present a replanning algorithm for a decision-theoretic hierarchical planner, illustrate the experimental methodology we designed to investigate its performance,...
Many critical decisions for individuals and organizations are often framed as preferential choices: the process of selecting the best option out of a set of alternatives. This pap...
Different evaluation measures assess different characteristics of machine learning algorithms. The empirical evaluation of algorithms and classifiers is a matter of on-going debat...
Marina Sokolova, Nathalie Japkowicz, Stan Szpakowi...
Through the automation of empirical evaluation we hope to alleviate evaluation problems encountered by software designers who are relatively new to the process. Barriers to good e...
Laurian Hobby, John Booker, D. Scott McCrickard, C...