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pro vyhledávání: '"Qu, Yuezhou"'
Gaussian Processes (GP) have become popular machine-learning methods for kernel-based learning on datasets with complicated covariance structures. In this paper, we present a novel extension to the GP framework using a contaminated normal likelihood
Externí odkaz:
http://arxiv.org/abs/2402.17570
