Semi-supervised Learning via Gaussian Processes

Neil D. LawrenceMichael I. Jordan
,  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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-lawrence-semisuper04, title = {Semi-supervised Learning via Gaussian Processes}, author = {Neil D. Lawrence and Michael I. Jordan}, pages = {753--760}, year = {}, editor = {}, volume = {17}, address = {Cambridge, MA}, url = {http://inverseprobability.com/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 %C Proceedings of Machine Learning Research %D %E %F pmlr-v-lawrence-semisuper04 %I PMLR %J Proceedings of Machine Learning Research %P 753--760 %U http://inverseprobability.com %V %W PMLR %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 - PY - DA - ED - ID - pmlr-v-lawrence-semisuper04 PB - PMLR SP - 753 DP - PMLR EP - 760 L1 - UR - http://inverseprobability.com/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.. (). Semi-supervised Learning via Gaussian Processes. , in PMLR :753-760

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