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ML
2008
ACM
100views Machine Learning» more  ML 2008»
13 years 11 months ago
Generalized ordering-search for learning directed probabilistic logical models
Abstract. Recently, there has been an increasing interest in directed probabilistic logical models and a variety of languages for describing such models has been proposed. Although...
Jan Ramon, Tom Croonenborghs, Daan Fierens, Hendri...
UAI
1997
14 years 7 days ago
A Bayesian Approach to Learning Bayesian Networks with Local Structure
Recently several researchers have investigated techniques for using data to learn Bayesian networks containing compact representations for the conditional probability distribution...
David Maxwell Chickering, David Heckerman, Christo...
ANLP
2000
106views more  ANLP 2000»
14 years 7 days ago
Exploiting auxiliary distributions in stochastic unification-based grammars
This paper describes a method for estimating conditional probability distributions over the parses of "unification-based" grammars which can utilize auxiliary distributi...
Mark Johnson, Stefan Riezler
WSCG
2004
217views more  WSCG 2004»
14 years 8 days ago
Blending Textured Images Using a Non-parametric Multiscale MRF Method
In this paper we describe a new method for improving the representation of textures in blends of multiple images based on a Markov Random Field (MRF) algorithm. We show that direc...
Bernard Tiddeman
ACL
2001
14 years 8 days ago
Joint and Conditional Estimation of Tagging and Parsing Models
This paper compares two different ways of estimating statistical language models. Many statistical NLP tagging and parsing models are estimated by maximizing the (joint) likelihoo...
Mark Johnson
ISIPTA
1999
IEEE
14 years 3 months ago
Nonlinear Filtering of Convex Sets of Probability Distributions
A solution is provided to the problem of computing a convex set of conditional probability distributions that characterize the state of a nonlinear dynamic system as it evolves in...
John Kenney, Wynn C. Stirling
CVPR
2003
IEEE
15 years 27 days ago
PAMPAS: Real-Valued Graphical Models for Computer Vision
Probabilistic models have been adopted for many computer vision applications, however inference in highdimensional spaces remains problematic. As the statespace of a model grows, ...
Michael Isard