Increasingly focus in the software comprehension community is shifting from representing the results of analysis in the graph and database domain to reflecting insights directly i...
In the TREC 2008, the team from the State University of New York at Buffalo participated in the Legal track and the Blog track. For the Legal track, we worked on the interactive s...
Jianqiang Wang, Ying Sun, Omar Mukhtar, Rohini K. ...
In this paper, we propose a model named Logical Markov Decision Processes with Negation for Relational Reinforcement Learning for applying Reinforcement Learning algorithms on the ...
Given a database with missing or uncertain content, our goal is to correct and fill the database by extracting specific information from a large corpus such as the Web, and to d...
Supervised learning from multiple labeling sources is an increasingly important problem in machine learning and data mining. This paper develops a probabilistic approach to this p...