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000888532 037__ $$aFZJ-2020-04996
000888532 1001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b0$$eCorresponding author
000888532 1112_ $$aIEEE International Geoscience and Remote Sensing Symposium (IGARSS)$$cOnline event$$d2020-09-27 - 2020-10-02$$gIGARSS 2020$$wOnline event
000888532 245__ $$aApproaching Remote Sensing Image Classification with Ensembles of Support Vector Machines on the D-Wave Quantum Annealer
000888532 260__ $$c2020
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000888532 520__ $$aSupport Vector Machine (SVM) is a popular supervised Machine Learning (ML) method that is widely used for classification and regression problems. Recently, a method to train SVMs on a D-Wave 2000Q Quantum Annealer (QA) was proposed for binary classification of some biological data. First, ensembles of weak quantum SVMs are generated by training each classifier on a disjoint training subset that can be fit into the QA. Then, the computed weak solutions are fused for making predictions on unseen data. In this work, the classification of Remote Sensing (RS) multispectral images with SVMs trained on a QA is discussed. Furthermore, an open code repository is released to facilitate an early entry into the practical application of QA, a new disruptive compute technology.
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000888532 7001_ $$0P:(DE-Juel1)167542$$aWillsch, Dennis$$b1
000888532 7001_ $$0P:(DE-Juel1)167543$$aWillsch, Madita$$b2
000888532 7001_ $$0P:(DE-Juel1)138295$$aMichielsen, Kristel$$b3
000888532 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b4
000888532 8564_ $$uhttps://juser.fz-juelich.de/record/888532/files/Cavallaro_IGARSS_2020.pdf$$yOpenAccess
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