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» Learning Rules and Their Exceptions
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ICMLA
2009
14 years 9 months ago
Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule
Languages that combine predicate logic with probabilities are needed to succinctly represent knowledge in many real-world domains. We consider a formalism based on universally qua...
Sriraam Natarajan, Prasad Tadepalli, Gautam Kunapu...
ECML
2001
Springer
15 years 4 months ago
A Framework for Learning Rules from Multiple Instance Data
Abstract. This paper proposes a generic extension to propositional rule learners to handle multiple-instance data. In a multiple-instance representation, each learning example is r...
Yann Chevaleyre, Jean-Daniel Zucker
BPM
2003
Springer
123views Business» more  BPM 2003»
15 years 3 months ago
A Top-Down Petri Net-Based Approach for Dynamic Workflow Modeling
A top-down approach for workflow design is proposed in the framework of Petri net theory. Simple but powerful refinement rules are proposed that guarantee soundness of the resultin...
Piotr Chrzastowski-Wachtel, Boualem Benatallah, Ra...
RELMICS
2000
Springer
15 years 3 months ago
Implication-with-possible-exceptions
Abstract. We introduce an implication-with-possible-exceptions and define validity of rules-withpossible-exceptions by means of the topological notion of a full subset. Our implica...
Nerman Jurrjus, Harrie C. M. de Swart
APIN
2000
155views more  APIN 2000»
14 years 11 months ago
Defeasible Logic on an Embedded Microcontroller
Defeasible logic is a system of reasoning in which rules have exceptions, and when rules conflict, the one that applies most specifically to the situation wins out. This paper repo...
Michael A. Covington