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» A theory of learning from different domains
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IJON
2010
181views more  IJON 2010»
14 years 8 months ago
Active learning with extremely sparse labeled examples
An active learner usually assumes there are some labeled data available based on which a moderate classifier is learned and then examines unlabeled data to manually label the mos...
Shiliang Sun, David R. Hardoon
SEMWEB
2004
Springer
15 years 3 months ago
Learning Meta-descriptions of the FOAF Network
We argue that in a distributed context, such as the Semantic Web, ontology engineers and data creators often cannot control (or even imagine) the possible uses their data or ontolo...
Gunnar Aastrand Grimnes, Peter Edwards, Alun D. Pr...
AIEDAM
1998
87views more  AIEDAM 1998»
14 years 9 months ago
Learning to set up numerical optimizations of engineering designs
Gradient-based numerical optimization of complex engineering designs offers the promise of rapidly producing better designs. However, such methods generally assume that the object...
Mark Schwabacher, Thomas Ellman, Haym Hirsh
CVPR
2009
IEEE
14 years 7 months ago
Learning IMED via shift-invariant transformation
The IMage Euclidean Distance (IMED) is a class of image metrics, in which the spatial relationship between pixels is taken into consideration. It was shown that calculating the IM...
Bing Sun, Jufu Feng, Liwei Wang
CVPR
2012
IEEE
13 years 14 days ago
Unsupervised feature learning framework for no-reference image quality assessment
In this paper, we present an efficient general-purpose objective no-reference (NR) image quality assessment (IQA) framework based on unsupervised feature learning. The goal is to...
Peng Ye, Jayant Kumar, Le Kang, David S. Doermann