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» Learning the Structure of Linear Latent Variable Models
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ICML
2004
IEEE
14 years 9 months ago
Hyperplane margin classifiers on the multinomial manifold
The assumptions behind linear classifiers for categorical data are examined and reformulated in the context of the multinomial manifold, the simplex of multinomial models furnishe...
Guy Lebanon, John D. Lafferty
CVPR
2009
IEEE
14 years 2 days ago
Robust unsupervised segmentation of degraded document images with topic models
Segmentation of document images remains a challenging vision problem. Although document images have a structured layout, capturing enough of it for segmentation can be difficult....
Timothy J. Burns, Jason J. Corso
CORR
2008
Springer
129views Education» more  CORR 2008»
13 years 9 months ago
Polynomial Linear Programming with Gaussian Belief Propagation
Abstract--Interior-point methods are state-of-the-art algorithms for solving linear programming (LP) problems with polynomial complexity. Specifically, the Karmarkar algorithm typi...
Danny Bickson, Yoav Tock, Ori Shental, Danny Dolev
CIDM
2009
IEEE
14 years 3 months ago
A new hybrid method for Bayesian network learning With dependency constraints
Abstract— A Bayes net has qualitative and quantitative aspects: The qualitative aspect is its graphical structure that corresponds to correlations among the variables in the Baye...
Oliver Schulte, Gustavo Frigo, Russell Greiner, We...
EMNLP
2007
13 years 10 months ago
Finding Good Sequential Model Structures using Output Transformations
In Sequential Viterbi Models, such as HMMs, MEMMs, and Linear Chain CRFs, the type of patterns over output sequences that can be learned by the model depend directly on the model...
Edward Loper