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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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Metric-Based Approaches for Semi-Supervised Regression and Classification

Metric-Based Approaches for Semi-Supervised Regression and Classification

(p.420) (p.421) 23 Metric-Based Approaches for Semi-Supervised Regression and Classification
Semi-Supervised Learning

Schuurmans Dale

Southey Finnegan

Wilkinson Dana

Guo Yuhong

The MIT Press

This chapter discusses the explicit relationship that must be asserted between labeled and unlabeled data, which is a requirement of semi-supervised learning methods. Semi-supervised model selection and regularization methods are presented here that instead require only that the labeled and unlabeled data are drawn from the same distribution. From this assumption, a metric can be constructed over hypotheses based on their predictions for unlabeled data. This metric can then be used to detect untrustworthy training error estimates, leading to model selection strategies that select the richest hypothesis class while providing theoretical guarantees against overfitting. This general approach is then adapted to regularization for supervised regression and supervised classification with probabilistic classifiers. The regularization adapts not only to the hypothesis class but also to the specific data sample provided, allowing for better performance than regularizers that account only for class complexity.

Keywords:   semi-supervised learning methods, semi-supervised model selection, regularization methods, training error estimates, overfitting, supervised regression, supervised classification, probabilistic classifiers

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