This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
In this paper we propose a nonparametric hypothesis test for stationarity based on local Fourier analysis. We employ a test statistic that measures the variation of time-localized...
Rapid advancements in positioning systems and wireless communications enable accurate tracking of continuously moving objects. This development poses new challenges to database te...
Shuqiao Guo, Zhiyong Huang, H. V. Jagadish, Beng C...
Abstract. We investigate the use of parameterized state machine models to drive integration testing, in the case where the models of components are not available beforehand. Theref...
Background: Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3...