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» Learning Classifiers from Semantically Heterogeneous Data
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GECCO
2005
Springer
162views Optimization» more  GECCO 2005»
15 years 12 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»
16 years 7 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»
16 years 2 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
15 years 11 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
15 years 6 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