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» Learning Rules and Their Exceptions
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ICMLA
2009
14 years 7 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 2 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 1 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 1 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 9 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