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Semi-Supervised Learning$
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Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien

Print publication date: 2006

Print ISBN-13: 9780262033589

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262033589.001.0001

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Gaussian Processes and the Null-Category Noise Model

Gaussian Processes and the Null-Category Noise Model

(p.136) (p.137) 8 Gaussian Processes and the Null-Category Noise Model
Semi-Supervised Learning

Lawrence Neil D.

Jordan Michael I.

The MIT Press

This chapter presents an augmentation of the standard probabilistic classification model which incorporates a null-category. Given a suitable probabilistic model for the model category, the chapter obtains a probabilistic counterpart of the margin. By combining this noise model with a Gaussian process classifier (GPC), it obtains a classification methodology that is simultaneously discriminative, semi-supervised, and Bayesian. The approach incorporates the cluster assumption without explicitly modeling the data density and without requiring specialized kernels. GPCs aim to predict the posterior probability of the class label yi given a covariate vector xi. Under the standard assumptions generally invoked by GPC practitioners, this posterior probability is unaffected by unlabeled data points, providing no role for unlabeled data.

Keywords:   probabilistic classification model, null-category, Gaussian process classifier, GPC, classification methodology, cluster assumption, unlabeled data

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