Siena / Cork / Palo Alto / Trondheim / Banff / Vancouver / Cambridge / Beijing / Toronto / Williamstown / San Diego / Chicago / Random Tree / /
Company
Neural Information Processing Systems / BP / CRF / Distributed Computing Systems / CRFs / Ubiquitous Systems / Wireless Communication Systems / Monte Carlo / Intel / /
Country
Italy / Norway / United States / Canada / China / Ireland / / /
Event
Product Recall / Product Issues / /
Facility
Engineering CSIRO ICT Centre Hong Kong University of Science / /
IndustryTerm
near real-time event detection / near real time / online event detection / gas monitoring sensor networks / numerical gradient algorithms / real time / wireless sensor networks / real-time event detection / real-world applications / large-scale sensor networks / large and dense networks / sensor networks / inference algorithm / Online detection phase / natural language processing / real-time decisions / online phase / gas leakage / approximate inference algorithm / sensor network / be optimized using numerical gradient algorithms / /
Organization
Computer Science and Engineering CSIRO ICT Centre Hong Kong University of Science and Technology Australia Hong Kong / Spatio-Temporal Event Detection Using Dynamic Conditional Random Fields Jie Yin Derek Hao Hu and Qiang Yang Information Engineering Laboratory Department of Computer Science / Artiļ¬cial Intelligence / /
Person
Alan S. Willsky / K. Stanek / T. Jaakkola / Joseph M. Hellerstein / W. Hong / S. Madden / M. Wainwright / Qiang Yang / /
Position
Statistician / DCRF model at each time slice / /
Product
F1-Score / /
ProvinceOrState
Alberta / British Columbia / Ontario / /
PublishedMedium
Machine Learning / /
Technology
RAM / Viterbi algorithm / inference algorithm / Machine Learning / approximate inference algorithm / natural language processing / STED algorithm / proposed algorithm / numerical gradient algorithms / radio frequency / Very Large Database / /
Spatio-Temporal Event Detection Using Dynamic Conditional Random Fields