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Salience assignment for multiple-instance regression

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dc.contributor.author Wagstaff, Kiri L.
dc.contributor.author Lane, Terran
dc.date.accessioned 2009-07-16T17:18:50Z
dc.date.available 2009-07-16T17:18:50Z
dc.date.issued 2007-06-24
dc.identifier.citation ICML 2007 Workshop on Constrained Optimization and Structured Output Spaces, Covallis, OR, June 24, 2007 en_US
dc.identifier.clearanceno 07-1809
dc.identifier.uri http://hdl.handle.net/2014/41349
dc.description.abstract We present a Multiple-Instance Learning (MIL) algorithm for determining the salience of each item in each bag with respect to the bag's real-valued label. We use an alternating-projections constrained optimization approach to simultaneously learn a regression model and estimate all salience values. We evaluate this algorithm on a significant real-world problem, crop yield modeling, and demonstrate that it provides more extensive, intuitive, and stable salience models than Primary-Instance Regression, which selects a single relevant item from each bag. en_US
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2007. en_US
dc.subject regression en_US
dc.subject relevance en_US
dc.subject crop yield prediction en_US
dc.title Salience assignment for multiple-instance regression en_US
dc.type Preprint en_US


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