Abstract
A new approach of input uncertainty classification is proposed in this paper. This approach develops a new technique which extends the support vector classification (SVC) by incorporating input uncertainties. Kernel functions can be used to generalize this proposed technique to non-linear models and the resulting optimization problem is a second order cone program with a unique solution. Results are shown to demonstrate how the technique is more robust when uncertainty information is available.
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Yang, J., Gunn, S. (2007). Exploiting Uncertain Data in Support Vector Classification. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74829-8_19
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DOI: https://doi.org/10.1007/978-3-540-74829-8_19
Publisher Name: Springer, Berlin, Heidelberg
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