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» Learning the Relative Importance of Features in Image Data
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SOFSEM
1999
Springer
14 years 24 days ago
Coherent Concepts, Robust Learning
We study learning scenarios in which multiple learners are involved and “nature” imposes some constraints that force the predictions of these learners to behave coherently. Thi...
Dan Roth, Dmitry Zelenko
CORR
2008
Springer
143views Education» more  CORR 2008»
13 years 8 months ago
Join Bayes Nets: A new type of Bayes net for relational data
Many real-world data are maintained in relational format, with different tables storing information about entities and their links or relationships. The structure (schema) of the ...
Oliver Schulte, Hassan Khosravi, Flavia Moser, Mar...
ICCV
2009
IEEE
1425views Computer Vision» more  ICCV 2009»
14 years 11 months ago
Fast Ray Features for Learning Irregular Shapes
We introduce a new class of image features, the Ray feature set, that consider image characteristics at distant contour points, capturing information which is difficult to repre...
Kevin Smith, Alan Carleton, Vincent Lepetit
MM
2005
ACM
171views Multimedia» more  MM 2005»
14 years 2 months ago
Semantic manifold learning for image retrieval
Learning the user’s semantics for CBIR involves two different sources of information: the similarity relations entailed by the content-based features, and the relevance relatio...
Yen-Yu Lin, Tyng-Luh Liu, Hwann-Tzong Chen
UIST
1996
ACM
14 years 21 days ago
Adding Imageability Features to Information Displays
Techniques for improving the imageability of an existing data visualisation are described. The aim is to make the visualisation more easily explored, navigated and remembered. Sta...
Matthew Chalmers, Robert Ingram, Christoph Pfrange...