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ICML
2009
IEEE
16 years 2 months ago
Exploiting sparse Markov and covariance structure in multiresolution models
We consider Gaussian multiresolution (MR) models in which coarser, hidden variables serve to capture statistical dependencies among the finest scale variables. Tree-structured MR ...
Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...
107
Voted
AAAI
2000
15 years 3 months ago
Information Extraction with HMM Structures Learned by Stochastic Optimization
Recent research has demonstrated the strong performance of hidden Markov models applied to information extraction--the task of populating database slots with corresponding phrases...
Dayne Freitag, Andrew McCallum
102
Voted
SSIAI
2000
IEEE
15 years 6 months ago
Unsupervised Dempster-Shafer Fusion of Dependent Sensors
This paper deals with the problem of statistical unsupervised fusion of dependent sensors with its potential applications to multisensor image segmentation. On the one hand, Bayes...
Wojciech Pieczynski
137
Voted
ECML
1998
Springer
15 years 5 months ago
Predicate Invention and Learning from Positive Examples Only
Previous bias shift approaches to predicate invention are not applicable to learning from positive examples only, if a complete hypothesis can be found in the given language, as ne...
Henrik Boström
100
Voted
ICIP
2000
IEEE
16 years 3 months ago
Hierarchical Image Probability (HIP) Models
We formulate a model for probability distributions on image spaces. We show that any distribution of images can be factored exactly into conditional distributions of feature vecto...
Clay Spence, Lucas C. Parra, Paul Sajda