Random set theory and problems of modeling
SIAM Review
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Varieties of ignorance and the need for well-founded theories
Information Sciences: an International Journal - Special issue on information sciences—past, present, and future
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Modeling uncertainty using partial information
Information Sciences: an International Journal
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Towards a Unified Modeling and Knowledge-Representation based on Lattice Theory: Computational Intelligence and Soft Computing Applications (Studies in Computational Intelligence)
Choquet weak convergence of capacity functionals of random sets
Information Sciences: an International Journal
Bayesian multiple instance learning: automatic feature selection and inductive transfer
Proceedings of the 25th international conference on Machine learning
Combining uncertainty and imprecision in models of medical diagnosis
Information Sciences: an International Journal
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Multiple instance learning (MIL) is a technique used for learning a target concept in the presence of noise or in a condition of uncertainty. While standard learning techniques present the learner with individual samples, MIL alternatively presents the learner with sets of samples. Although sets are the primary elements used for analysis in MIL, research in this area has focused on using standard analysis techniques. In the following, a random set framework for multiple instance learning (RSF-MIL) is proposed that can directly perform analysis on sets. The proposed method uses random sets and fuzzy measures to model the MIL problem, thus providing a more natural mathematical framework, a more general MIL solution, and a more versatile learning tool. Comparative experimental results using RSF-MIL are presented for benchmark data sets. RSF-MIL is further compared to the state-of-the-art in landmine detection using ground penetrating radar data.