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2004

Semi-supervised Learning via Gaussian Processes

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Semi-supervised Learning via Gaussian Processes
We present a probabilistic approach to learning a Gaussian Process classifier in the presence of unlabeled data. Our approach involves a "null category noise model" (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.
Neil D. Lawrence, Michael I. Jordan
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where NIPS
Authors Neil D. Lawrence, Michael I. Jordan
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