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 cate- gorical 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 compar- ative results for the semi-supervised classification of handwritten digits.

Authors

• Neil Lawrence
• Michael Jordan

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