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» Learning Markov Logic Networks Using Structural Motifs
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DSN
2002
IEEE
13 years 10 months ago
Model Checking Performability Properties
Model checking has been introduced as an automated technique to verify whether functional properties, expressed in a formal logic like computational tree logic (CTL), do hold in a...
Boudewijn R. Haverkort, Lucia Cloth, Holger Herman...
BMCBI
2010
229views more  BMCBI 2010»
13 years 5 months ago
Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
Martin Paluszewski, Thomas Hamelryck
LREC
2010
197views Education» more  LREC 2010»
13 years 7 months ago
Automatic Annotation of Co-Occurrence Relations
We introduce a method for automatically labelling edges of word co-occurrence graphs with semantic relations. Therefore we only make use of training data already contained within ...
Dirk Goldhahn, Uwe Quasthoff
CVPR
2008
IEEE
14 years 8 days ago
Scene understanding with discriminative structured prediction
Spatial priors play crucial roles in many high-level vision tasks, e.g. scene understanding. Usually, learning spatial priors relies on training a structured output model. In this...
Jinhui Yuan, Jianmin Li, Bo Zhang
DKE
2007
95views more  DKE 2007»
13 years 5 months ago
Strategies for improving the modeling and interpretability of Bayesian networks
One of the main factors for the knowledge discovery success is related to the comprehensibility of the patterns discovered by applying data mining techniques. Amongst which we can...
Ádamo L. de Santana, Carlos Renato Lisboa F...