We present an approach for learning models that obtain accurate classification of large scale data objects, collected in spatiotemporal domains. The model generation is structured ...
Igor Vainer, Sarit Kraus, Gal A. Kaminka, Hamutal ...
Given a collection of Boolean spatio-temporal(ST) event types, the cascading spatio-temporal pattern (CSTP) discovery process finds partially ordered subsets of event-types whose ...
Pradeep Mohan, Shashi Shekhar, James A. Shine, Jam...
As data sources become larger and more complex, the ability to effectively explore and analyze patterns amongst varying sources becomes a critical bottleneck in analytic reasoning...
Ross Maciejewski, Stephen Rudolph, Ryan Hafen, Ahm...
Learning Bayesian networks from data has been studied extensively in the evolutionary algorithm communities [Larranaga96, Wong99]. We have previously explored extending some of the...
Detection and analysis of ocean surface phenomena have so far relied on manual analysis of long sequences of satellite images or images produced from the mathematical models. In t...
Veena Moolani, Ramprasad Balasubramanian, Li Shen,...