Abstract
This paper introduces copula functions and the use of the Gaussian copula function to model probabilistic dependencies in supervised classification tasks. A copula is a distribution function with the implicit capacity to model non linear dependencies via concordance measures, such as Kendall’s τ. Hence, this work studies the performance of a simple probabilistic classifier based on the Gaussian copula function. Without additional preprocessing of the source data, a supervised pixel classifier is tested with a 50-images benchmark; the experiments show this simple classifier has an excellent performance.
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Salinas-Gutiérrez, R., Hernández-Aguirre, A., Rivera-Meraz, M.J.J., Villa-Diharce, E.R. (2010). Supervised Probabilistic Classification Based on Gaussian Copulas. In: Sidorov, G., Hernández Aguirre, A., Reyes García, C.A. (eds) Advances in Soft Computing. MICAI 2010. Lecture Notes in Computer Science(), vol 6438. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16773-7_9
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DOI: https://doi.org/10.1007/978-3-642-16773-7_9
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