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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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Introduction to Semi-Supervised Learning

Introduction to Semi-Supervised Learning

(p.1) 1 Introduction to Semi-Supervised Learning
Semi-Supervised Learning

Chapelle Olivier

Schölkopf Bernhard

Zien Alexander

The MIT Press

This chapter first presents definitions of supervised and unsupervised learning in order to understand the nature of semi-supervised learning (SSL). SSL is halfway between supervised and unsupervised learning. In addition to unlabeled data, the algorithm is provided with some supervision information—but not necessarily for all examples. Often, this information will be the targets associated with some of the examples. Other forms of partial supervision are possible. For example, there may be constraints such as “these points have (or do not have) the same target.” The different setting corresponds to a different view of semi-supervised learning: In succeeding chapters, SSL is seen as unsupervised learning guided by constraints. A problem related to SSL was introduced by Vapnik several decades ago—transductive learning. In this setting, a labeled training set and an unlabeled test set are provided. The idea of transduction is to perform predictions only for the test points.

Keywords:   semi-supervised learning, SSL, unlabeled data, algorithm, supervision information, partial supervision, constraints, Vapnik, transductive learning

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