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» Learning Classifiers from Semantically Heterogeneous Data
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GECCO
2005
Springer
162views Optimization» more  GECCO 2005»
14 years 3 months ago
A framework for learning coordinated behavior
We sketch a framework for learning structured coordinated behavior, specifically the tactical behavior of Experimental Unmanned Vehicles (XUVs). We conceptualize an XUV unit as a ...
Albert C. Esterline, Chafic BouSaba, Abdollah Homa...
KDD
2009
ACM
173views Data Mining» more  KDD 2009»
14 years 10 months ago
The offset tree for learning with partial labels
We present an algorithm, called the offset tree, for learning in situations where a loss associated with different decisions is not known, but was randomly probed. The algorithm i...
Alina Beygelzimer, John Langford
CVPR
2010
IEEE
1135views Computer Vision» more  CVPR 2010»
14 years 5 months ago
Towards Weakly Supervised Semantic Segmentation by Means of Multiple Instance and Multitask Learning.
We address the task of learning a semantic segmentation from weakly supervised data. Our aim is to devise a system that predicts an object label for each pixel by making use of on...
Alexander Vezhnevets, Joachim Buhmann
WWW
2010
ACM
14 years 2 months ago
Web-scale knowledge extraction from semi-structured tables
A wealth of knowledge is encoded in the form of tables on the World Wide Web. We propose a classification algorithm and a rich feature set for automatically recognizing layout tab...
Eric Crestan, Patrick Pantel
SIGIR
2008
ACM
13 years 10 months ago
Semi-supervised spam filtering: does it work?
The results of the 2006 ECML/PKDD Discovery Challenge suggest that semi-supervised learning methods work well for spam filtering when the source of available labeled examples diff...
Mona Mojdeh, Gordon V. Cormack