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» Learning and prediction of slip from visual information
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RECOMB
2002
Springer
14 years 9 months ago
From promoter sequence to expression: a probabilistic framework
We present a probabilistic framework that models the process by which transcriptional binding explains the mRNA expression of different genes. Our joint probabilistic model unifie...
Eran Segal, Yoseph Barash, Itamar Simon, Nir Fried...
IV
2008
IEEE
129views Visualization» more  IV 2008»
14 years 3 months ago
113 Years of Physical Review: Using Flow Maps to Show Temporal and Topical Citation Patterns
We visualize 113 years of bibliographic data from the American Physical Society. The 389,899 documents are laid out in a two dimensional time-topic reference system. The citations...
Bruce W. Herr, Russell J. Duhon, Katy Börner,...
WWW
2007
ACM
14 years 9 months ago
Predicting clicks: estimating the click-through rate for new ads
Search engine advertising has become a significant element of the Web browsing experience. Choosing the right ads for the query and the order in which they are displayed greatly a...
Matthew Richardson, Ewa Dominowska, Robert Ragno
ICML
2003
IEEE
14 years 10 months ago
Regression Error Characteristic Curves
Receiver Operating Characteristic (ROC) curves provide a powerful tool for visualizing and comparing classification results. Regression Error Characteristic (REC) curves generaliz...
Jinbo Bi, Kristin P. Bennett
CVIU
2008
126views more  CVIU 2008»
13 years 9 months ago
Optimising dynamic graphical models for video content analysis
A key problem in video content analysis using dynamic graphical models is to learn a suitable model structure given some observed visual data. We propose a Completed Likelihood AI...
Tao Xiang, Shaogang Gong