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» Learning and Inference with Constraints
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ML
2002
ACM
220views Machine Learning» more  ML 2002»
15 years 5 months ago
Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities
I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This probabilisti...
Peter Sollich
177
Voted
KI
2010
Springer
15 years 4 months ago
An Extensible Modular Recognition Concept That Makes Activity Recognition Practical
Abstract. In mobile and ubiquitous computing, there is a strong need for supporting different users with different interests, needs, and demands. Activity recognition systems for c...
Martin Berchtold, Matthias Budde, Hedda Rahel Schm...
PAMI
2010
188views more  PAMI 2010»
15 years 4 months ago
A Unified Probabilistic Framework for Spontaneous Facial Action Modeling and Understanding
—Facial expression is a natural and powerful means of human communication. Recognizing spontaneous facial actions, however, is very challenging due to subtle facial deformation, ...
Yan Tong, Jixu Chen, Qiang Ji
162
Voted
CVPR
2010
IEEE
15 years 4 months ago
Part and appearance sharing: Recursive Compositional Models for multi-view
We propose Recursive Compositional Models (RCMs) for simultaneous multi-view multi-object detection and parsing (e.g. view estimation and determining the positions of the object s...
Long Zhu, Yuanhao Chen, Antonio Torralba, William ...
198
Voted
CVPR
2010
IEEE
15 years 4 months ago
Adaptive pose priors for pictorial structures
Pictorial structure (PS) models are extensively used for part-based recognition of scenes, people, animals and multi-part objects. To achieve tractability, the structure and param...
Benjamin Sapp, Chris Jordan, Ben Taskar