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» Learning Probabilistic Models of Contours
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EMNLP
2004
14 years 11 months ago
Max-Margin Parsing
We present a novel discriminative approach to parsing inspired by the large-margin criterion underlying support vector machines. Our formulation uses a factorization analogous to ...
Ben Taskar, Dan Klein, Mike Collins, Daphne Koller...
ICML
2006
IEEE
15 years 10 months ago
Hidden process models
We introduce Hidden Process Models (HPMs), a class of probabilistic models for multivariate time series data. The design of HPMs has been motivated by the challenges of modeling h...
Rebecca Hutchinson, Tom M. Mitchell, Indrayana Rus...
ML
2006
ACM
14 years 10 months ago
Using duration models to reduce fragmentation in audio segmentation
We investigate explicit segment duration models in addressing the problem of fragmentation in musical audio segmentation. The resulting probabilistic models are optimised using Mar...
Samer A. Abdallah, Mark B. Sandler, Christophe Rho...
ICCV
2009
IEEE
16 years 3 months ago
Learning a dense multi-view representation for detection, viewpoint classification and synthesis of object categories
Recognizing object classes and their 3D viewpoints is an important problem in computer vision. Based on a partbased probabilistic representation [31], we propose a new 3D object...
Hao Su, Min Sun, Li Fei-Fei, Silvio Savarese
CVPR
2008
IEEE
16 years 1 days ago
Learning for stereo vision using the structured support vector machine
We present a random field based model for stereo vision with explicit occlusion labeling in a probabilistic framework. The model employs non-parametric cost functions that can be ...
Yunpeng Li, Daniel P. Huttenlocher