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IJAR
2010
130views more  IJAR 2010»
14 years 8 months ago
Learning locally minimax optimal Bayesian networks
We consider the problem of learning Bayesian network models in a non-informative setting, where the only available information is a set of observational data, and no background kn...
Tomi Silander, Teemu Roos, Petri Myllymäki
TIP
2010
164views more  TIP 2010»
14 years 4 months ago
A Marked Point Process for Modeling Lidar Waveforms
Lidar waveforms are 1D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful ...
Clément Mallet, Florent Lafarge, Michel Rou...
LWA
2008
14 years 11 months ago
Labeling Clusters - Tagging Resources
In order to support the navigation in huge document collections efficiently, tagged hierarchical structures can be used. Often, multiple tags are used to describe resources. For u...
Korinna Bade, Andreas Nürnberger
RECSYS
2009
ACM
15 years 4 months ago
Uncovering functional dependencies in MDD-compiled product catalogues
A functional dependency is a logical relationship amongst the attributes that define a table of data. Specifically, a functional dependency holds when the values of a subset of ...
Tarik Hadzic, Barry O'Sullivan
CIKM
2008
Springer
14 years 12 months ago
Learning a two-stage SVM/CRF sequence classifier
Learning a sequence classifier means learning to predict a sequence of output tags based on a set of input data items. For example, recognizing that a handwritten word is "ca...
Guilherme Hoefel, Charles Elkan