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Skew gaussian processes for classification

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dc.contributor.author Benavoli, Alessio
dc.contributor.author Azzimonti, Dario
dc.contributor.author Piga, Dario
dc.date.accessioned 2020-09-22T10:04:23Z
dc.date.available 2020-09-22T10:04:23Z
dc.date.issued 2020
dc.identifier.uri http://hdl.handle.net/10344/9245
dc.description peer-reviewed en_US
dc.description.abstract Gaussian processes (GPs) are distributions over functions, which provide a Bayesian nonparametric approach to regression and classification. In spite of their success, GPs have limited use in some applications, for example, in some cases a symmetric distribution with respect to its mean is an unreasonable model. This implies, for instance, that the mean and the median coincide, while the mean and median in an asymmetric (skewed) distribution can be different numbers. In this paper, we propose skew-Gaussian processes (SkewGPs) as a non-parametric prior over functions. A SkewGP extends the multivariate unified skew normal distribution over finite dimensional vectors to a stochastic processes. The SkewGP class of distributions includes GPs and, therefore, SkewGPs inherit all good properties of GPs and increase their flexibility by allowing asymmetry in the probabilistic model. By exploiting the fact that SkewGP and probit likelihood are conjugate model, we derive closed form expressions for the marginal likelihood and predictive distribution of this new nonparametric classifier. We verify empirically that the proposed SkewGP classifier provides a better performance than a GP classifier based on either Laplace’s method or expectation propagation. en_US
dc.language.iso eng en_US
dc.publisher Springer en_US
dc.relation.ispartofseries Machine Learning;
dc.subject Skew Gaussian Process en_US
dc.subject Nonparametric en_US
dc.subject Classifier en_US
dc.title Skew gaussian processes for classification en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.1007/s10994-020-05906-3
dc.contributor.sponsor SUPSI en_US
dc.relation.projectid 407540_167199 / 1 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US


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