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» Learning in Gaussian Markov random fields
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JMLR
2008
230views more  JMLR 2008»
14 years 11 months ago
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of...
Michael Collins, Amir Globerson, Terry Koo, Xavier...
CVPR
2007
IEEE
16 years 1 months ago
Spatio-Temporal Markov Random Field for Video Denoising
This paper presents a novel spatio-temporal Markov random field (MRF) for video denoising. Two main issues are addressed in this paper, namely, the estimation of noise model and t...
Jia Chen, Chi-Keung Tang
ICML
2005
IEEE
16 years 13 days ago
Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields
We present a directed Markov random field (MRF) model that combines n-gram models, probabilistic context free grammars (PCFGs) and probabilistic latent semantic analysis (PLSA) fo...
Shaojun Wang, Shaomin Wang, Russell Greiner, Dale ...
SIGIR
2009
ACM
15 years 6 months ago
An improved markov random field model for supporting verbose queries
Recent work in supervised learning of term-based retrieval models has shown significantly improved accuracy can often be achieved via better model estimation [2, 10, 11, 17]. In ...
Matthew Lease
NIPS
2004
15 years 1 months ago
Semi-Markov Conditional Random Fields for Information Extraction
We describe semi-Markov conditional random fields (semi-CRFs), a conditionally trained version of semi-Markov chains. Intuitively, a semiCRF on an input sequence x outputs a "...
Sunita Sarawagi, William W. Cohen