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KDD
2010
ACM
274views Data Mining» more  KDD 2010»
15 years 3 months 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
CVPR
2009
IEEE
15 years 10 months ago
Markov Chain Monte Carlo Combined with Deterministic Methods for Markov Random Field Optimization
Many vision problems have been formulated as en- ergy minimization problems and there have been signif- icant advances in energy minimization algorithms. The most widely-used energ...
Wonsik Kim (Seoul National University), Kyoung Mu ...
TSP
2008
101views more  TSP 2008»
14 years 11 months ago
Optimal Node Density for Detection in Energy-Constrained Random Networks
The problem of optimal node density maximizing the Neyman-Pearson detection error exponent subject to a constraint on average (per node) energy consumption is analyzed. The spatial...
Animashree Anandkumar, Lang Tong, Ananthram Swami
IROS
2007
IEEE
148views Robotics» more  IROS 2007»
15 years 6 months ago
Tractable probabilistic models for intention recognition based on expert knowledge
— Intention recognition is an important topic in human-robot cooperation that can be tackled using probabilistic model-based methods. A popular instance of such methods are Bayes...
Oliver C. Schrempf, David Albrecht, Uwe D. Hanebec...
ACSAC
2003
IEEE
15 years 5 months ago
Automatic Reassembly of Document Fragments via Context Based Statistical Models
Reassembly of fragmented objects from a collection of randomly mixed fragments is a common problem in classical forensics. In this paper we address the digital forensic equivalent...
Kulesh Shanmugasundaram, Nasir D. Memon