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» Learning Generative Models via Discriminative Approaches
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ICML
2009
IEEE
14 years 9 months ago
Optimized expected information gain for nonlinear dynamical systems
This paper addresses the problem of active model selection for nonlinear dynamical systems. We propose a novel learning approach that selects the most informative subset of time-d...
Alberto Giovanni Busetto, Cheng Soon Ong, Joachim ...
TSE
2010
155views more  TSE 2010»
13 years 7 months ago
A Comparison of Six UML-Based Languages for Software Process Modeling
— Describing and managing activities, resources and constraints of software development processes is a challenging goal for many organizations. A first generation of Software Pro...
Reda Bendraou, Jean-Marc Jézéquel, M...
AAI
2005
117views more  AAI 2005»
13 years 9 months ago
Machine Learning in Hybrid Hierarchical and Partial-Order Planners for Manufacturing Domains
The application of AI planning techniques to manufacturing systems is being widely deployed for all the tasks involved in the process, from product design to production planning an...
Susana Fernández, Ricardo Aler, Daniel Borr...
GRAPHICSINTERFACE
2000
13 years 10 months ago
Using a 3D Puzzle as a Metaphor for Learning Spatial Relations
We introduce a new metaphor for learning spatial relations--the 3D puzzle. With this metaphor users learn spatial relations by assembling a geometric model themselves. For this pu...
Bernhard Preim, Felix Ritter, Oliver Deussen, Thom...
MICRO
2009
IEEE
113views Hardware» more  MICRO 2009»
14 years 3 months ago
Portable compiler optimisation across embedded programs and microarchitectures using machine learning
Building an optimising compiler is a difficult and time consuming task which must be repeated for each generation of a microprocessor. As the underlying microarchitecture changes...
Christophe Dubach, Timothy M. Jones, Edwin V. Boni...