Learning Rules from Multiple Instance Data : Issues and Algorithms

@inproceedings{Chevaleyre2001LearningRF,
  title={Learning Rules from Multiple Instance Data : Issues and Algorithms},
  author={Yann Chevaleyre},
  year={2001}
}
This paper proposes a generic extension to propositional rule learners to handle multiple-instance data. Multipleinstance representation lies between propositional and first-order representation and offers a tradeoff between the two. This naive extension, which is implemented on the rule learner RIPPER, encounters several pitfalls which are analyzed. Then, solutions are described to avoid these pitfalls. 
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