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001     888532
005     20210130011007.0
024 7 _ |a 2128/26385
|2 Handle
037 _ _ |a FZJ-2020-04996
100 1 _ |a Cavallaro, Gabriele
|0 P:(DE-Juel1)171343
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|e Corresponding author
111 2 _ |a IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
|g IGARSS 2020
|c Online event
|d 2020-09-27 - 2020-10-02
|w Online event
245 _ _ |a Approaching Remote Sensing Image Classification with Ensembles of Support Vector Machines on the D-Wave Quantum Annealer
260 _ _ |c 2020
336 7 _ |a Conference Paper
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336 7 _ |a Other
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520 _ _ |a Support 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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700 1 _ |a Willsch, Dennis
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700 1 _ |a Willsch, Madita
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700 1 _ |a Michielsen, Kristel
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700 1 _ |a Riedel, Morris
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|b 4
856 4 _ |u https://juser.fz-juelich.de/record/888532/files/Cavallaro_IGARSS_2020.pdf
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909 C O |o oai:juser.fz-juelich.de:888532
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