Semi-supervised Learning via <span>G</span>aussian Processes

Neil D. Lawrence; Michael I. Jordan

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Semi-supervised Learning via Gaussian Processes

Neil D. Lawrence, Michael I. Jordan

Advances in Neural Information Processing Systems, MIT Press 17:753-760, 2005.

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 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.

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Cite this Paper

BibTeX


@InProceedings{Lawrence:semisuper04,
  title = 	 {Semi-supervised Learning via Gaussian Processes},
  author = 	 {Lawrence, Neil D. and Jordan, Michael I.},
  booktitle = 	 {Advances in Neural Information Processing Systems},
  pages = 	 {753--760},
  year = 	 {2005},
  editor = 	 {Saul, Lawrence and Weiss, Yair and Bouttou, Léon},
  volume = 	 {17},
  address = 	 {Cambridge, MA},
  publisher =    {MIT Press},
  pdf = 	 {https://lawrennd.github.io/publications/files/ncnm.pdf},
  url = 	 {/publications/lawrence-semisuper04.html},
  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 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.}
}

Endnote

%0 Conference Paper
%T Semi-supervised Learning via Gaussian Processes
%A Neil D. Lawrence
%A Michael I. Jordan
%B Advances in Neural Information Processing Systems
%D 2005
%E Lawrence Saul
%E Yair Weiss
%E Léon Bouttou	
%F Lawrence:semisuper04
%I MIT Press
%P 753--760
%U /publications/lawrence-semisuper04.html
%V 17
%X 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.

RIS


TY  - CPAPER
TI  - Semi-supervised Learning via Gaussian Processes
AU  - Neil D. Lawrence
AU  - Michael I. Jordan
BT  - Advances in Neural Information Processing Systems
DA  - 2005/01/01
ED  - Lawrence Saul
ED  - Yair Weiss
ED  - Léon Bouttou	
ID  - Lawrence:semisuper04
PB  - MIT Press
VL  - 17
SP  - 753
EP  - 760
L1  - https://lawrennd.github.io/publications/files/ncnm.pdf
UR  - /publications/lawrence-semisuper04.html
AB  - 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.
ER  -

APA


Lawrence, N.D. & Jordan, M.I.. (2005). Semi-supervised Learning via Gaussian Processes. Advances in Neural Information Processing Systems 17:753-760 Available from /publications/lawrence-semisuper04.html.