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Semi-Supervised Domain Adaptation with Non-Parametric Copulas
[article]
2013
arXiv
pre-print
A new framework based on the theory of copulas is proposed to address semi- supervised domain adaptation problems. The presented method factorizes any multivariate density into a product of marginal distributions and bivariate cop- ula functions. Therefore, changes in each of these factors can be detected and corrected to adapt a density model accross different learning domains. Impor- tantly, we introduce a novel vine copula model, which allows for this factorization in a non-parametric
arXiv:1301.0142v1
fatcat:jyni2boikjhntosqwsxcux5ltq