Abstract
In this chapter, we give an overview of different approaches developed in semi-supervised learning, as well as different assumptions leading to these methods. We particularly consider the margin as an indicator of confidence which constitutes the working hypothesis of algorithms that search the decision boundary on low density regions. Following this assumption, we present a bound over the error probability of the voted classifier on the examples for whose margins are above a fixed threshold. As an application, we detail a self-learning algorithm which iteratively assigns pseudo-labels to the set of unlabeled training examples that have their margin above a threshold obtained from this bound and also present a multiview extension of this method.
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Notes
- 1.
A random walk models systems with discrete dynamics composed of a successive random steps (Montroll 1956). The Markov nature of the process reflects the full decorrelation between the random steps.
- 2.
The transductive learning is a special case of semi-supervised learning, as the unlabeled examples are known a priori and are observed by the learning algorithm during the training stage.
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© 2015 Springer International Publishing Switzerland
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Amini, MR., Usunier, N. (2015). Semi-Supervised Learning. In: Learning with Partially Labeled and Interdependent Data. Springer, Cham. https://doi.org/10.1007/978-3-319-15726-9_3
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DOI: https://doi.org/10.1007/978-3-319-15726-9_3
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15725-2
Online ISBN: 978-3-319-15726-9
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