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• W1522567247 abstract "Gaussian processes have successfully been used to learn preferences among entities as they provide nonparametric Bayesian approaches for model selection and probabilistic inference. For many entities encountered in real-world applications, however, there are complex relations between them. In this paper, we present a preference model which incorporates information on relations among entities. Specifically, we propose a probabilistic relational kernel model for preference learning based on Silva et al.’s mixed graph Gaussian processes: a new prior distribution, enhanced with relational graph kernels, is proposed to capture the correlations between preferences. Empirical analysis on the LETOR datasets demonstrates that relational information can improve the performance of preference learning.KeywordsGaussian ProcessLatent FunctionMultivariate Gaussian DistributionPreference LearningPreference DegreeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
• W1522567247 created "2016-06-24" @default.
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• W1522567247 date "2009-01-01" @default.
• W1522567247 modified "2023-10-15" @default.
• W1522567247 title "Learning Preferences with Hidden Common Cause Relations" @default.
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• W1522567247 doi "https://doi.org/10.1007/978-3-642-04180-8_61" @default.
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