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• W2104156750 abstract "We present a probabilistic approach to learning a Gaussian Process classifier in the presence of unlabeled data. Our approach involves a null category noise (NCNM) inspired by ordered categorical noise models. The noise model reflects an assumption that the data density is lower between the class-conditional densities. We illustrate our approach on a toy problem and present comparative results for the semi-supervised classification of handwritten digits." @default.
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• W2104156750 date "2004-12-01" @default.
• W2104156750 modified "2023-09-29" @default.
• W2104156750 title "Semi-supervised Learning via Gaussian Processes" @default.
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