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@INPROCEEDINGS{Memon:842290,
      author       = {Memon, Mohammad Shahbaz and Cavallaro, Gabriele and Riedel,
                      Morris and Neukirchen, Helmut},
      title        = {{F}acilitating efficient data analysis of remotely sensed
                      images using standards-based parameter sweep models},
      publisher    = {IEEE},
      reportid     = {FZJ-2018-00537},
      pages        = {3680-3683},
      year         = {2017},
      comment      = {[Proceedings] - IEEE, 2017. - ISBN 978-1-5090-4951-6},
      booktitle     = {[Proceedings] - IEEE, 2017. - ISBN
                       978-1-5090-4951-6},
      abstract     = {Classification of remote sensing images often use Support
                      Vector Machines (SVMs) that require an n-fold
                      cross-validation phase in order to do model selection. This
                      phase is characterized by sweeping through a wide set of
                      parameter combinations of SVM kernel and cost parameters. As
                      a consequence this process is computationally expensive but
                      represents a principled way of tuning a model for better
                      accuracy and to prevent overfitting together with
                      regularization that is in SVMs inherently solved in the
                      optimization. Since the cross-validation technique is done
                      in a principled way also known as
                      $\‘gridsearch\’,$ we aim at supporting remote
                      sensing scientists in two ways. Firstly by reducing the
                      time-to-solution of the cross-validation by applying
                      state-of-the-art parallel processing methods because the
                      sweep of parameters and cross-validation runs itself can be
                      nicely parallelized. Secondly by reducing manual labour by
                      automating the parallel submission processes since manually
                      performing cross-validation is very time consuming,
                      unintuitive, and error-prone especially in large-scale
                      cluster or supercomputing environments (e.g., batch job
                      scripts, node/core/task parameters, etc.).},
      month         = {Jul},
      date          = {2017-07-23},
      organization  = {2017 IEEE International Geoscience and
                       Remote Sensing Symposium, Fort Worth,
                       TX (USA), 23 Jul 2017 - 28 Jul 2017},
      cin          = {JSC},
      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 / PUB:(DE-HGF)7},
      doi          = {10.1109/IGARSS.2017.8127797},
      url          = {https://juser.fz-juelich.de/record/842290},
}