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» Generating UML Models from Domain Patterns
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NIPS
2008
15 years 4 months ago
Non-stationary dynamic Bayesian networks
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
Joshua W. Robinson, Alexander J. Hartemink
154
Voted
SASO
2008
IEEE
15 years 9 months ago
Spatial Self-Organization of Heterogeneous, Modular Architectures
Abstract— On the one hand, natural phenomena of spontaneous pattern formation are generally random and repetitive, whereas, on the other hand, complicated heterogeneous architect...
René Doursat
128
Voted
ARTMED
2006
75views more  ARTMED 2006»
15 years 3 months ago
Semi-automatic learning of simple diagnostic scores utilizing complexity measures
Objective: Knowledge acquisition and maintenance in medical domains with a large application domain ontology is a difficult task. To reduce knowledge elicitation costs, semiautoma...
Martin Atzmüller, Joachim Baumeister, Frank P...
126
Voted
NIPS
2007
15 years 4 months ago
Learning Visual Attributes
We present a probabilistic generative model of visual attributes, together with an efficient learning algorithm. Attributes are visual qualities of objects, such as ‘red’, ...
Vittorio Ferrari, Andrew Zisserman
WCE
2007
15 years 4 months ago
Automatic State Machine Induction for String Recognition
—One problem of generating a model to recognize any string is how to generate one that is generalized enough to accept strings with similar patterns and, at the same time, is spe...
Boontee Kruatrachue, Nattachat Pantrakarn, Kritawa...