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» Probabilistic model for contextual retrieval
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NIPS
2008
13 years 9 months ago
Structured ranking learning using cumulative distribution networks
Ranking is at the heart of many information retrieval applications. Unlike standard regression or classification in which we predict outputs independently, in ranking we are inter...
Jim C. Huang, Brendan J. Frey
RSKT
2009
Springer
14 years 2 months ago
Learning Optimal Parameters in Decision-Theoretic Rough Sets
A game-theoretic approach for learning optimal parameter values for probabilistic rough set regions is presented. The parameters can be used to define approximation regions in a p...
Joseph P. Herbert, Jingtao Yao
ICPR
2004
IEEE
14 years 8 months ago
BTF Image Space Utmost Compression and Modelling Method
The bidirectional texture function (BTF) describes texture appearance variations due to varying illumination and viewing conditions. This function is acquired by large number of m...
Jirí Filip, Michael Arnold, Michal Haindl
EMNLP
2008
13 years 9 months ago
Modeling Annotators: A Generative Approach to Learning from Annotator Rationales
A human annotator can provide hints to a machine learner by highlighting contextual "rationales" for each of his or her annotations (Zaidan et al., 2007). How can one ex...
Omar Zaidan, Jason Eisner
ISM
2008
IEEE
101views Multimedia» more  ISM 2008»
14 years 2 months ago
Photo Context as a Bag of Words
In the recent years, photo context metadata (e.g., date, GPS coordinates) have been proved to be useful in the management of personal photos. However, these metadata are still poo...
Windson Viana, Samira Hammiche, Marlène Vil...