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A comparative study of supervised learning techniques for data-driven haptic simulation
conference contribution
posted on 2011-01-01, 00:00 authored by Wael Abdelrahman, Sara Farag, Saeid Nahavandi, Douglas CreightonDouglas CreightonThis paper focuses on the choice of a supervised learning algorithm and possible data preprocessing in the domain of data-driven haptic simulation. This is done through a comparison of the performance of different supervised learning techniques with and without data preprocessing. The simulation of haptic interactions with deformable objects using data-driven methods has emerged as an alternative to parametric methods. The accuracy of the simulation depends on the empirical data and the learning method. Several methods were suggested in the literature and here we provide a comparison between their performance and applicability to this domain. We selected four examples to be compared: singular learning mechanism which is artificial neural networks (ANN), attribute selection followed by ANN learning process, ensemble of multiple learning techniques, and attribute selection followed by the learning ensemble. These methods performance was compared in the domain of simulating multiple interactions with a deformable object with nonlinear material behavior.
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IEEE International Conference of Systems, Man, and Cybernetics (2011 : Anchorage, Alaska)Pagination
2842 - 2846Publisher
IEEELocation
Anchorage, AlaskaPlace of publication
[Anchorage, Alaska]Start date
2011-10-09End date
2011-10-12ISBN-13
9781457706523ISBN-10
1457706520Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2011, IEEETitle of proceedings
SMC 2011 : Conference proceeding of the 2011 International Conference on Systems, Man, and CyberneticsUsage metrics
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