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KDD
2010
ACM
274views Data Mining» more  KDD 2010»
14 years 3 days ago
Grafting-light: fast, incremental feature selection and structure learning of Markov random fields
Feature selection is an important task in order to achieve better generalizability in high dimensional learning, and structure learning of Markov random fields (MRFs) can automat...
Jun Zhu, Ni Lao, Eric P. Xing
TIP
2008
133views more  TIP 2008»
13 years 8 months ago
A Recursive Model-Reduction Method for Approximate Inference in Gaussian Markov Random Fields
This paper presents recursive cavity modeling--a principled, tractable approach to approximate, near-optimal inference for large Gauss-Markov random fields. The main idea is to su...
Jason K. Johnson, Alan S. Willsky
IFM
2010
Springer
190views Formal Methods» more  IFM 2010»
13 years 6 months ago
On Model Checking Techniques for Randomized Distributed Systems
Abstract. The automata-based model checking approach for randomized distributed systems relies on an operational interleaving semantics of the system by means of a Markov decision ...
Christel Baier
IMAMS
2003
125views Mathematics» more  IMAMS 2003»
13 years 9 months ago
A Graph-Spectral Method for Surface Height Recovery
This paper describes a graph-spectral method for 3D surface integration. The algorithm takes as its input a 2D field of surface normal estimates, delivered, for instance, by a sh...
Antonio Robles-Kelly, Edwin R. Hancock
PAMI
2007
176views more  PAMI 2007»
13 years 7 months ago
Approximate Labeling via Graph Cuts Based on Linear Programming
A new framework is presented for both understanding and developing graph-cut based combinatorial algorithms suitable for the approximate optimization of a very wide class of MRFs ...
Nikos Komodakis, Georgios Tziritas