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» TRUST-TECH based Methods for Optimization and Learning
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CVPR
2012
IEEE
13 years 3 months ago
Weakly supervised structured output learning for semantic segmentation
We address the problem of weakly supervised semantic segmentation. The training images are labeled only by the classes they contain, not by their location in the image. On test im...
Alexander Vezhnevets, Vittorio Ferrari, Joachim M....
102
Voted
ICML
2009
IEEE
15 years 7 months ago
Fast evolutionary maximum margin clustering
The maximum margin clustering approach is a recently proposed extension of the concept of support vector machines to the clustering problem. Briefly stated, it aims at finding a...
Fabian Gieseke, Tapio Pahikkala, Oliver Kramer
117
Voted
CORR
2010
Springer
253views Education» more  CORR 2010»
15 years 25 days ago
Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition
Adaptive sparse coding methods learn a possibly overcomplete set of basis functions, such that natural image patches can be reconstructed by linearly combining a small subset of t...
Koray Kavukcuoglu, Marc'Aurelio Ranzato, Yann LeCu...
111
Voted
SWWS
2008
15 years 2 months ago
A Harmony based Adaptive Ontology Mapping Approach
- Ontology mapping seeks to find semantic correspondences between similar elements of different ontologies. Ontology mapping is critical to achieve semantic interoperability in the...
Ming Mao, Yefei Peng, Michael Spring
GECCO
2007
Springer
171views Optimization» more  GECCO 2007»
15 years 6 months ago
Toward a better understanding of rule initialisation and deletion
A number of heuristics have been used in Learning Classifier Systems to initialise parameters of new rules, to adjust fitness of parent rules when they generate offspring, and ...
Tim Kovacs, Larry Bull