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» Causal inference using the algorithmic Markov condition
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ICDAR
2007
IEEE
15 years 4 months ago
Handwritten Word Recognition Using Conditional Random Fields
The paper describes a lexicon driven approach for word recognition on handwritten documents using Conditional Random Fields(CRFs). CRFs are discriminative models and do not make a...
Shravya Shetty, Harish Srinivasan, Sargur N. Sriha...
ICCV
2003
IEEE
15 years 11 months ago
Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters
Recent stereo algorithms have achieved impressive results by modelling the disparity image as a Markov Random Field (MRF). An important component of an MRF-based approach is the i...
Marshall F. Tappen, William T. Freeman
KDD
2008
ACM
174views Data Mining» more  KDD 2008»
15 years 10 months ago
Automatic identification of quasi-experimental designs for discovering causal knowledge
Researchers in the social and behavioral sciences routinely rely on quasi-experimental designs to discover knowledge from large databases. Quasi-experimental designs (QEDs) exploi...
David D. Jensen, Andrew S. Fast, Brian J. Taylor, ...
CDC
2010
IEEE
123views Control Systems» more  CDC 2010»
14 years 4 months ago
Causal observability of nonlinear time-delay systems with unknown inputs
This paper investigates the problem of causal observability of the states and unknown inputs of nonlinear time-delay systems. Using the theory of non-commutative rings, the nonline...
Gang Zheng, Jean-Pierre Barbot, Driss Boutat, Thie...
ICML
2008
IEEE
15 years 10 months ago
Beam sampling for the infinite hidden Markov model
The infinite hidden Markov model is a nonparametric extension of the widely used hidden Markov model. Our paper introduces a new inference algorithm for the infinite Hidden Markov...
Jurgen Van Gael, Yunus Saatci, Yee Whye Teh, Zoubi...