Semi-Supervised Learning via Gaussian Processes
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 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.
Cite
Text
Lawrence and Jordan. "Semi-Supervised Learning via Gaussian Processes." Neural Information Processing Systems, 2004.Markdown
[Lawrence and Jordan. "Semi-Supervised Learning via Gaussian Processes." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/lawrence2004neurips-semisupervised/)BibTeX
@inproceedings{lawrence2004neurips-semisupervised,
title = {{Semi-Supervised Learning via Gaussian Processes}},
author = {Lawrence, Neil D. and Jordan, Michael I.},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {753-760},
url = {https://mlanthology.org/neurips/2004/lawrence2004neurips-semisupervised/}
}