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CVPR
2011
IEEE
15 years 2 months ago
What You Saw is Not What You Get: Domain Adaptation Using Asymmetric Kernel Transforms
In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features...
Brian Kulis, Kate Saenko, Trevor Darrell
KDD
2006
ACM
240views Data Mining» more  KDD 2006»
16 years 7 months ago
Adaptive event detection with time-varying poisson processes
Time-series of count data are generated in many different contexts, such as web access logging, freeway traffic monitoring, and security logs associated with buildings. Since this...
Alexander T. Ihler, Jon Hutchins, Padhraic Smyth
EDM
2008
127views Data Mining» more  EDM 2008»
15 years 8 months ago
Adaptive Test Design with a Naive Bayes Framework
Bayesian graphical models are commonly used to build student models from data. A number of standard algorithms are available to train Bayesian models from student skills assessment...
Michel C. Desmarais, Alejandro Villarreal, Michel ...
CVPR
2005
IEEE
16 years 9 months ago
Semi-Supervised Adapted HMMs for Unusual Event Detection
We address the problem of temporal unusual event detection. Unusual events are characterized by a number of features (rarity, unexpectedness, and relevance) that limit the applica...
Dong Zhang, Daniel Gatica-Perez, Samy Bengio, Iain...
CVRMED
1997
Springer
15 years 11 months ago
Medical image segmentation using topologically adaptable surfaces
E cient and powerful topologically adaptable deformable surfaces can be created by embedding and de ning discrete deformable surface models in terms of an A ne Cell Decomposition (...
Tim McInerney, Demetri Terzopoulos