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PAMI
2008
176views more  PAMI 2008»
13 years 9 months ago
Learning Flexible Features for Conditional Random Fields
Abstract-- Extending traditional models for discriminative labeling of structured data to include higher-order structure in the labels results in an undesirable exponential increas...
Liam Stewart, Xuming He, Richard S. Zemel
KDD
2010
ACM
274views Data Mining» more  KDD 2010»
14 years 1 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
ECCV
2008
Springer
14 years 11 months ago
Hierarchical Support Vector Random Fields: Joint Training to Combine Local and Global Features
Abstract. Recently, impressive results have been reported for the detection of objects in challenging real-world scenes. Interestingly however, the underlying models vary greatly e...
Paul Schnitzspan, Mario Fritz, Bernt Schiele
IVC
2002
90views more  IVC 2002»
13 years 9 months ago
Force field energy functionals for image feature extraction
Ears are an emergent biometric accruing application advantages including no requirement for subject contact and acquisition without demand. To recognize a subject's ear, we a...
David J. Hurley, Mark S. Nixon, John N. Carter
SIAMSC
2008
99views more  SIAMSC 2008»
13 years 9 months ago
Mixed Multiscale Finite Element Methods for Stochastic Porous Media Flows
In this paper, we propose a stochastic mixed multiscale finite element method. The proposed method solves the stochastic porous media flow equation on the coarse grid using a set ...
J. E. Aarnes, Yalchin Efendiev