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ML
2010
ACM
151views Machine Learning» more  ML 2010»
15 years 3 months ago
Inductive transfer for learning Bayesian networks
In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with...
Roger Luis, Luis Enrique Sucar, Eduardo F. Morales
ICMCS
2005
IEEE
183views Multimedia» more  ICMCS 2005»
15 years 10 months ago
Sports Event Recognition Using Layered HMMS
The recognition of events in video data is a subject of much current interest. In this paper, we address several issues related to this topic. The first one is overfitting when ...
Mark Barnard, Jean-Marc Odobez
SCIA
2009
Springer
261views Image Analysis» more  SCIA 2009»
15 years 9 months ago
Dense and Deformable Motion Segmentation for Wide Baseline Images
In this paper we describe a dense motion segmentation method for wide baseline image pairs. Unlike many previous methods our approach is able to deal with deforming motions and lar...
Juho Kannala, Esa Rahtu, Sami S. Brandt, Janne Hei...
JMLR
2012
13 years 7 months ago
Structured Output Learning with High Order Loss Functions
Often when modeling structured domains, it is desirable to leverage information that is not naturally expressed as simply a label. Examples include knowledge about the evaluation ...
Daniel Tarlow, Richard S. Zemel
CVPR
2012
IEEE
13 years 7 months ago
Unsupervised learning of translation invariant occlusive components
We study unsupervised learning of occluding objects in images of visual scenes. The derived learning algorithm is based on a probabilistic generative model which parameterizes obj...
Zhenwen Dai, Jörg Lücke