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@INPROCEEDINGS{Cavallaro:888532,
      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},
      reportid     = {FZJ-2020-04996},
      year         = {2020},
      abstract     = {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.},
      month         = {Sep},
      date          = {2020-09-27},
      organization  = {IEEE International Geoscience and
                       Remote Sensing Symposium (IGARSS),
                       Online event (Online event), 27 Sep
                       2020 - 2 Oct 2020},
      subtyp        = {After Call},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512) / 511 - Computational Science and Mathematical
                      Methods (POF3-511)},
      pid          = {G:(DE-HGF)POF3-512 / G:(DE-HGF)POF3-511},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/888532},
}