A probabilistic concept annotation for IT service desk tickets
Ea-Ee Jan, Kuan-Yu Chen, et al.
ESAIR 2014
We present a new approach to lightweight intelligent transportation systems. Our approach does not rely on traditional expensive infrastructures, but rather on advanced machine learning algorithms. It takes images from traffic cameras at a limited number of locations and estimates the traffic over the entire road network. Our approach features two main algorithms. The first is a probabilistic vehicle counting algorithm from low-quality images that falls into the category of unsupervised learning. The other is a network inference algorithm based on an inverse Markov chain formulation that infers the traffic at arbitrary links from a limited number of observations. We evaluated our approach on two different traffic data sets, one acquired in Nairobi, Kenya, and the other in Kyoto, Japan.
Ea-Ee Jan, Kuan-Yu Chen, et al.
ESAIR 2014
Hidemasa Muta, Rudy Raymond, et al.
WSC 2014
Tetsuro Morimura, Masashi Sugiyama, et al.
UAI 2010
Tsuyoshi Idé, Sei Kato
SDM 2009