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Conjunctive formulation of the random set framework for multiple instance learning: Application to remote sensing

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2 Author(s)
Bolton, J. ; Comput. Sci. & Intell. Lab., Univ. of Florida, Gainesville, FL, USA ; Gader, P.

Multiple instance learning (MIL) is a widely researched learning paradigm that allows a machine learning algorithm to learn target concepts from data with uncertain class labels. The random set framework for multiple instance learning (RSF-MIL) makes use of the random set to learn in this scenario of uncertainty. Previous models used assumptions that imposed a disjunctive relationship between the simple concepts learned (which compose the target concept). In the following, a conjunctive formulation of RSF-MIL is proposed and investigated. Results illustrate the utility of the conjunctive and disjunctive formulations of RSF-MIL and the scenarios when each is applicable.

Published in:

Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International

Date of Conference:

24-29 July 2011