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» Learning and Inference with Constraints
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ICML
2008
IEEE
14 years 11 months ago
Modeling interleaved hidden processes
Hidden Markov models assume that observations in time series data stem from some hidden process that can be compactly represented as a Markov chain. We generalize this model by as...
Niels Landwehr
NIPS
2000
13 years 11 months ago
On Reversing Jensen's Inequality
Jensen's inequality is a powerful mathematical tool and one of the workhorses in statistical learning. Its applications therein include the EM algorithm, Bayesian estimation ...
Tony Jebara, Alex Pentland
BMCBI
2010
118views more  BMCBI 2010»
13 years 10 months ago
From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification
Background: Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limit...
Bram Slabbinck, Willem Waegeman, Peter Dawyndt, Pa...
CVPR
2011
IEEE
13 years 6 months ago
Enforcing Similarity Constraints with Integer Programming for Better Scene Text Recognition
The recognition of text in everyday scenes is made difficult by viewing conditions, unusual fonts, and lack of linguistic context. Most methods integrate a priori appearance info...
David Smith, Jacqueline Feild, Eric Learned-Miller
COLT
2006
Springer
14 years 7 days ago
Can Entropic Regularization Be Replaced by Squared Euclidean Distance Plus Additional Linear Constraints
There are two main families of on-line algorithms depending on whether a relative entropy or a squared Euclidean distance is used as a regularizer. The difference between the two f...
Manfred K. Warmuth