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Title: Salience assignment for multiple-instance regression
Authors: Wagstaff, Kiri L.
Lane, Terran
Keywords: regression
crop yield prediction
Issue Date: 24-Jun-2007
Publisher: Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2007.
Citation: ICML 2007 Workshop on Constrained Optimization and Structured Output Spaces, Covallis, OR, June 24, 2007
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.
Appears in Collections:JPL TRS 1992+

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