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| 001 | 890967 | ||
| 005 | 20230111074306.0 | ||
| 024 | 7 | _ | |a 10.1109/IGARSS39084.2020.9323544 |2 doi |
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| 100 | 1 | _ | |a Cavallaro, Gabriele |0 P:(DE-Juel1)171343 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a 2020 IEEE International Geoscience and Remote Sensing Symposium |g IGARSS 2020 |c Online event |d 2020-09-26 - 2020-10-02 |w Hawaii |
| 245 | _ | _ | |a Approaching Remote Sensing Image Classification with Ensembles of Support Vector Machines on the D-Wave Quantum Annealer |
| 260 | _ | _ | |c 2020 |b IEEE |
| 300 | _ | _ | |a 1973 - 1976 |
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| 520 | _ | _ | |a Support Vector Machine (SVM) is a popular supervised MachineLearning (ML) method that is widely used for classificationand regression problems. Recently, a method to trainSVMs on a D-Wave 2000Q Quantum Annealer (QA) was proposedfor binary classification of some biological data. First,ensembles of weak quantum SVMs are generated by trainingeach classifier on a disjoint training subset that can be fitinto the QA. Then, the computed weak solutions are fusedfor making predictions on unseen data. In this work, the classificationof Remote Sensing (RS) multispectral images withSVMs trained on a QA is discussed. Furthermore, an opencode repository is released to facilitate an early entry into thepractical application of QA, a new disruptive compute technology. |
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| 700 | 1 | _ | |a Riedel, Morris |0 P:(DE-Juel1)132239 |b 4 |u fzj |
| 773 | _ | _ | |a 10.1109/IGARSS39084.2020.9323544 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/890967/files/Cavallaro_Gabriele_IGARSS_2020.pdf |y OpenAccess |
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