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A probabilistic support vector machine for uncertain data

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2 Author(s)
Jing-Lin Yang ; Department of MEEM, City University of Hongkong, Hongkong SAR, China ; Han-Xiong Li

A probabilistic support vector machine (PSVM) is proposed for classification of data with uncertainties. Performance of the traditional SVM algorithm is very sensitive to uncertainties. The noises in input space will cause uncertainties of the mapping in feature space. The traditional SVM algorithm may not be effective when uncertainty is large. A new probabilistic optimization is proposed to determine the decision boundary. The minimal distance is described probabilistically by its probability distribution function. Finally an artificial dataset and a real life dataset from UCI machine learning database are used to demonstrate the effectiveness of the proposed PSVM.

Published in:

2009 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications

Date of Conference:

11-13 May 2009