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ICML
2004
IEEE
14 years 8 months ago
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when longra...
Charles A. Sutton, Khashayar Rohanimanesh, Andrew ...
ICPR
2002
IEEE
14 years 8 months ago
Bayesian Networks as Ensemble of Classifiers
Classification of real-world data poses a number of challenging problems. Mismatch between classifier models and true data distributions on one hand and the use of approximate inf...
Ashutosh Garg, Vladimir Pavlovic, Thomas S. Huang
AAAI
1998
13 years 8 months ago
Probabilistic Frame-Based Systems
Two of the most important threads of work in knowledge representation today are frame-based representation systems (FRS's) and Bayesian networks (BNs). FRS's provide an ...
Daphne Koller, Avi Pfeffer
UAI
1997
13 years 8 months ago
A Scheme for Approximating Probabilistic Inference
This paper describes a class ofprobabilistic approximation algorithms based on bucket elimination which o er adjustable levels of accuracy ande ciency. We analyzethe approximation...
Rina Dechter, Irina Rish
ECCV
2000
Springer
14 years 9 months ago
Non-linear Bayesian Image Modelling
In recent years several techniques have been proposed for modelling the low-dimensional manifolds, or `subspaces', of natural images. Examples include principal component anal...
Christopher M. Bishop, John M. Winn