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A comparative study of supervised learning techniques for data-driven haptic simulation

Abdelrahman, Wael, Farag, Sara, Nahavandi, Saeid and Creighton, Douglas 2011, A comparative study of supervised learning techniques for data-driven haptic simulation, in SMC 2011 : Conference proceeding of the 2011 International Conference on Systems, Man, and Cybernetics, IEEE, [Anchorage, Alaska], pp. 2842-2846.

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Title A comparative study of supervised learning techniques for data-driven haptic simulation
Author(s) Abdelrahman, Wael
Farag, Sara
Nahavandi, Saeid
Creighton, DouglasORCID iD for Creighton, Douglas orcid.org/0000-0002-9217-1231
Conference name IEEE International Conference of Systems, Man, and Cybernetics (2011 : Anchorage, Alaska)
Conference location Anchorage, Alaska
Conference dates 9-12 Oct. 2011
Title of proceedings SMC 2011 : Conference proceeding of the 2011 International Conference on Systems, Man, and Cybernetics
Editor(s) [Unknown]
Publication date 2011
Conference series IEEE International Conference of Systems, Man, and Cybernetics
Start page 2842
End page 2846
Total pages 5
Publisher IEEE
Place of publication [Anchorage, Alaska]
Keyword(s) data-driven simulation
haptics
machine learning
Summary This 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.
ISBN 1457706520
9781457706523
Language eng
Field of Research 080111 Virtual Reality and Related Simulation
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2011, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30042217

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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