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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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Transductive Inference and Semi-Supervised Learning

Transductive Inference and Semi-Supervised Learning

Chapter:
(p.452) (p.453) 24 Transductive Inference and Semi-Supervised Learning
Source:
Semi-Supervised Learning
Author(s):

Vapnik Vladimir

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262033589.003.0024

This chapter discusses the difference between transductive inference and semi-supervised learning. It argues that transductive inference captures the intrinsic properties of the mechanism for extracting additional information from the unlabeled data. It also shows an important role of transduction for creating noninductive models of inference. In transductive inference the goal is to classify the given u test vectors of interest while in semi-supervised learning the goal is to find the function that minimizes the functional. Semi-supervised learning can be seen as being related to a particular setting of transductive learning. From a conceptual point of view, transductive inference contains important elements of a new philosophy of inference and this is the subject of this discussion.

Keywords:   transductive inference, semi-supervised learning, intrinsic properties, unlabeled data, transduction, noninductive models of inference, new philosophy of inference

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