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» Model Uncertainty in Classical Conditioning
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COMMA
2010
13 years 3 months ago
Probabilistic Semantics for the Carneades Argument Model Using Bayesian Networks
Abstract. This paper presents a technique with which instances of argument structures in the Carneades model can be given a probabilistic semantics by translating them into Bayesia...
Matthias Grabmair, Thomas F. Gordon, Douglas Walto...
IOR
2011
152views more  IOR 2011»
13 years 3 months ago
Risk-Averse Two-Stage Stochastic Linear Programming: Modeling and Decomposition
We formulate a risk-averse two-stage stochastic linear programming problem in which unresolved uncertainty remains after the second stage. The objective function is formulated as ...
Naomi Miller, Andrzej Ruszczynski
ICMCS
2007
IEEE
143views Multimedia» more  ICMCS 2007»
14 years 2 months ago
Hidden Conditional Random Fields for Meeting Segmentation
Automatic segmentation and classification of recorded meetings provides a basis towards understanding the content of a meeting. It enables effective browsing and querying in a me...
Stephan Reiter, Björn Schuller, Gerhard Rigol...
PAMI
2010
238views more  PAMI 2010»
13 years 6 months ago
Tracking Motion, Deformation, and Texture Using Conditionally Gaussian Processes
—We present a generative model and inference algorithm for 3D nonrigid object tracking. The model, which we call G-flow, enables the joint inference of 3D position, orientation, ...
Tim K. Marks, John R. Hershey, Javier R. Movellan
CVBIA
2005
Springer
14 years 1 months ago
Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines
Abstract. Markov Random Fields (MRFs) are a popular and wellmotivated model for many medical image processing tasks such as segmentation. Discriminative Random Fields (DRFs), a dis...
Chi-Hoon Lee, Mark Schmidt, Albert Murtha, Aalo Bi...