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Abstract: This paper addresses the issue of multipleinstance induction of rules in the presence of noise. It first proposes a multiple-instance extensions of rule-based learning algorithms. Then, it shows what kind of noise can appear in multiple-instance data, and how to handle it theoretically. Finally, it describes the implementation of such a noise-tolerant multiple instance learner, and shows its performance on several problems, including the well-known mutagenesis prediction task. 1. (Update)
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BibTeX entry: (Update)
CHEVALEYRE,Y .AND ZUCKER, J. 2000. Noise-tolerant rule induction from multi-instance data. In Proceedings of the ICML-2000 workshop on Attribute-Value and Relational Learning: Crossing the Boundaries, L. De Raedt and S. Kramer, Eds. http://citeseer.ist.psu.edu/chevaleyre00noisetolerant.html More
@misc{ chevaleyre00noisetolerant,
author = "Y. CHEVALEYRE and J. AND",
title = "Noise-tolerant rule induction from multi-instance data",
text = "CHEVALEYRE,Y .AND ZUCKER, J. 2000. Noise-tolerant rule induction from multi-instance
data. In Proceedings of the ICML-2000 workshop on Attribute-Value and Relational
Learning: Crossing the Boundaries, L. De Raedt and S. Kramer, Eds.",
year = "2000",
url = "citeseer.ist.psu.edu/chevaleyre00noisetolerant.html" }
Citations (may not include all citations):537 A Theory of the Learnable (context) - Valiant - 1984
35 Multiple-instance learning for natural scene classification - Maron, Ratan - 1998
27 A framework for multiple-instance learning - Lozano-Prez - 1998
18 Tractable induction and classification in first order logic .. - Sebag, Rouveirol - 1997
17 Learning from ambiguity - Maron - 1998
11 Changes of representation for efficient learning in structur.. (context) - Zucker, Ganascia - 1996
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