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ICML
2009
IEEE
16 years 7 months ago
On primal and dual sparsity of Markov networks
Sparsity is a desirable property in high dimensional learning. The 1-norm regularization can lead to primal sparsity, while max-margin methods achieve dual sparsity. Combining the...
Jun Zhu, Eric P. Xing
NAACL
2007
15 years 8 months ago
Lexicalized Markov Grammars for Sentence Compression
We present a sentence compression system based on synchronous context-free grammars (SCFG), following the successful noisy-channel approach of (Knight and Marcu, 2000). We define...
Michel Galley, Kathleen McKeown
ICIP
2007
IEEE
16 years 8 months ago
Image Denoising with Nonparametric Hidden Markov Trees
We develop a hierarchical, nonparametric statistical model for wavelet representations of natural images. Extending previous work on Gaussian scale mixtures, wavelet coefficients ...
Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jord...
AI
2008
Springer
15 years 6 months ago
Reachability analysis of uncertain systems using bounded-parameter Markov decision processes
Verification of reachability properties for probabilistic systems is usually based on variants of Markov processes. Current methods assume an exact model of the dynamic behavior a...
Di Wu, Xenofon D. Koutsoukos
ML
2006
ACM
131views Machine Learning» more  ML 2006»
15 years 6 months ago
Markov logic networks
We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge b...
Matthew Richardson, Pedro Domingos