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FTCGV
2011
122views more  FTCGV 2011»
14 years 2 months ago
Structured Learning and Prediction in Computer Vision
Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structur...
Sebastian Nowozin, Christoph H. Lampert
CVPR
2007
IEEE
16 years 26 days ago
Learning GMRF Structures for Spatial Priors
The goal of this paper is to find sparse and representative spatial priors that can be applied to part-based object localization. Assuming a GMRF prior over part configurations, w...
Lie Gu, Eric P. Xing, Takeo Kanade
ICCV
2007
IEEE
16 years 24 days ago
Active Learning with Gaussian Processes for Object Categorization
Discriminative methods for visual object category recognition are typically non-probabilistic, predicting class labels but not directly providing an estimate of uncertainty. Gauss...
Ashish Kapoor, Kristen Grauman, Raquel Urtasun, Tr...
RECOMB
2004
Springer
15 years 11 months ago
Learning Regulatory Network Models that Represent Regulator States and Roles
Abstract. We present an approach to inferring probabilistic models of generegulatory networks that is intended to provide a more mechanistic representation of transcriptional regul...
Keith Noto, Mark Craven
FGR
2008
IEEE
214views Biometrics» more  FGR 2008»
15 years 5 months ago
Normalized LDA for semi-supervised learning
Linear Discriminant Analysis (LDA) has been a popular method for feature extracting and face recognition. As a supervised method, it requires manually labeled samples for training...
Bin Fan, Zhen Lei, Stan Z. Li