Uncertainty and sensitivity analysis issues in support vector machines

Authors:
Theodore B. Trafalis;Jin Park
Affiliations:
School of Industrial Engineering, University of Oklahoma, Norman, OK;School of Industrial Engineering, University of Oklahoma, Norman, OK
Venue:
ICS'06 Proceedings of the 10th WSEAS international conference on Systems
Year:
2006

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Abstract

Robust optimization and sensitivity analysis techniques are applied to the support vector machine (SVM) learning problem. Perturbations of input data and model parameters are considered. This approach determines how the level of noise of data and model parameters influences the SVM solution. Examples illustrate the above methodology.