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@INPROCEEDINGS{Cavallaro:890967,
      author       = {Cavallaro, Gabriele and Willsch, Dennis and Willsch, Madita
                      and Michielsen, Kristel and Riedel, Morris},
      title        = {{A}pproaching {R}emote {S}ensing {I}mage {C}lassification
                      with {E}nsembles of {S}upport {V}ector {M}achines on the
                      {D}-{W}ave {Q}uantum {A}nnealer},
      publisher    = {IEEE},
      reportid     = {FZJ-2021-01283},
      pages        = {1973 - 1976},
      year         = {2020},
      abstract     = {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.},
      month         = {Sep},
      date          = {2020-09-26},
      organization  = {2020 IEEE International Geoscience and
                       Remote Sensing Symposium, Online event
                       (Hawaii), 26 Sep 2020 - 2 Oct 2020},
      cin          = {JSC},
      ddc          = {610},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512)},
      pid          = {G:(DE-HGF)POF3-512},
      typ          = {PUB:(DE-HGF)8},
      UT           = {WOS:000664335302007},
      doi          = {10.1109/IGARSS39084.2020.9323544},
      url          = {https://juser.fz-juelich.de/record/890967},
}