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» Support Vector Machines for 3D Shape Processing
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NIPS
2001
14 years 11 months ago
Covariance Kernels from Bayesian Generative Models
We propose the framework of mutual information kernels for learning covariance kernels, as used in Support Vector machines and Gaussian process classifiers, from unlabeled task da...
Matthias Seeger
FGR
2006
IEEE
131views Biometrics» more  FGR 2006»
15 years 3 months ago
Haar Features for FACS AU Recognition
We examined the effectiveness of using Haar features and the Adaboost boosting algorithm for FACS action unit (AU) recognition. We evaluated both recognition accuracy and processi...
Jacob Whitehill, Christian W. Omlin
NIPS
2001
14 years 11 months ago
Estimating Car Insurance Premia: a Case Study in High-Dimensional Data Inference
Estimating insurance premia from data is a difficult regression problem for several reasons: the large number of variables, many of which are discrete, and the very peculiar shape...
Nicolas Chapados, Yoshua Bengio, Pascal Vincent, J...
ECCV
2004
Springer
15 years 11 months ago
On the Significance of Real-World Conditions for Material Classification
Classifying materials from their appearance is a challenging problem, especially if illumination and pose conditions are permitted to change: highlights and shadows caused by 3D st...
Eric Hayman, Barbara Caputo, Mario Fritz, Jan-Olof...
ECCV
2000
Springer
15 years 2 months ago
Anti-Faces for Detection
This paper offers a novel detection method, which works well even in the case of a complicated image collection – for instance, a frontal face under a large class of linear tran...
Daniel Keren, Margarita Osadchy, Craig Gotsman