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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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An Augmented PAC Model for Semi-Supervised Learning

An Augmented PAC Model for Semi-Supervised Learning

(p.396) (p.397) 22 An Augmented PAC Model for Semi-Supervised Learning
Semi-Supervised Learning

Balcan Maria-Florina

Blum Avrim

The MIT Press

This chapter describes an augmented version of the PAC model, designed with semi-supervised learning in mind, that can be used to help think about the problem of learning from labeled and unlabeled data and many of the different approaches taken. The model provides a unified framework for analyzing when and why unlabeled data can help, in which one can discuss both sample-complexity and algorithmic issues. The model described here can be viewed as an extension of the standard PAC model, where a compatibility function is also proposed—a type of compatibility that one believes the target concept should have with the underlying distribution of data. Unlabeled data are potentially helpful in this setting because they allow one to estimate compatibility over the space of hypotheses.

Keywords:   PAC model, semi-supervised learning, problem of learning, unlabeled data, sample-complexity, algorithmic issues, compatibility function

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