TY  - CONF
AU  - Memon, Mohammad Shahbaz
AU  - Cavallaro, Gabriele
AU  - Riedel, Morris
AU  - Neukirchen, Helmut
TI  - Facilitating efficient data analysis of remotely sensed images using standards-based parameter sweep models
PB  - IEEE
M1  - FZJ-2018-00537
SP  - 3680-3683
PY  - 2017
AB  - 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.).
T2  - 2017 IEEE International Geoscience and Remote Sensing Symposium
CY  - 23 Jul 2017 - 28 Jul 2017, Fort Worth, TX (USA)
Y2  - 23 Jul 2017 - 28 Jul 2017
M2  - Fort Worth, TX, USA
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
DO  - DOI:10.1109/IGARSS.2017.8127797
UR  - https://juser.fz-juelich.de/record/842290
ER  -