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» Learning Classifiers from Semantically Heterogeneous Data
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JMLR
2008
104views more  JMLR 2008»
13 years 7 months ago
Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2
In this paper, the naive credal classifier, which is a set-valued counterpart of naive Bayes, is extended to a general and flexible treatment of incomplete data, yielding a new cl...
Giorgio Corani, Marco Zaffalon
IJCV
2007
163views more  IJCV 2007»
13 years 7 months ago
Semantic Modeling of Natural Scenes for Content-Based Image Retrieval
In this paper, we present a novel image representation that renders it possible to access natural scenes by local semantic description. Our work is motivated by the continuing effo...
Julia Vogel, Bernt Schiele
WWW
2004
ACM
14 years 8 months ago
Web taxonomy integration using support vector machines
We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only currently pervasive on the web, but also important to the eme...
Dell Zhang, Wee Sun Lee
AAAI
2011
12 years 7 months ago
Heterogeneous Transfer Learning with RBMs
A common approach in machine learning is to use a large amount of labeled data to train a model. Usually this model can then only be used to classify data in the same feature spac...
Bin Wei, Christopher Pal
ECCV
2008
Springer
14 years 9 months ago
Beyond Nouns: Exploiting Prepositions and Comparative Adjectives for Learning Visual Classifiers
Learning visual classifiers for object recognition from weakly labeled data requires determining correspondence between image regions and semantic object classes. Most approaches u...
Abhinav Gupta, Larry S. Davis