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» Answer Sets for Propositional Theories
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ILP
2004
Springer
15 years 3 months ago
First Order Random Forests with Complex Aggregates
Random forest induction is a bagging method that randomly samples the feature set at each node in a decision tree. In propositional learning, the method has been shown to work well...
Celine Vens, Anneleen Van Assche, Hendrik Blockeel...
ATAL
2010
Springer
14 years 10 months ago
Complexity of judgment aggregation: safety of the agenda
Aggregating the judgments of a group of agents regarding a set of interdependent propositions can lead to inconsistent outcomes. One of the parameters involved is the agenda, the ...
Ulle Endriss, Umberto Grandi, Daniele Porello
SARA
2007
Springer
15 years 3 months ago
Reformulation for Extensional Reasoning
Relational databases have had great industrial success in computer science. The power of the paradigm is made clear both by its widespread adoption and by theoretical analysis. Tod...
Timothy L. Hinrichs, Michael R. Genesereth
AIIA
2005
Springer
15 years 3 months ago
Handling Continuous-Valued Attributes in Incremental First-Order Rules Learning
Machine Learning systems are often distinguished according to the kind of representation they use, which can be either propositional or first-order logic. The framework working wi...
Teresa Maria Altomare Basile, Floriana Esposito, N...
AAAI
2004
14 years 11 months ago
Towards Efficient Sampling: Exploiting Random Walk Strategies
From a computational perspective, there is a close connection between various probabilistic reasoning tasks and the problem of counting or sampling satisfying assignments of a pro...
Wei Wei, Jordan Erenrich, Bart Selman