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2019 | OriginalPaper | Buchkapitel

2. Multiview Semi-supervised Learning

verfasst von : Shiliang Sun, Liang Mao, Ziang Dong, Lidan Wu

Erschienen in: Multiview Machine Learning

Verlag: Springer Singapore

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Abstract

Semi-supervised learning is concerned with such learning scenarios where only a small portion of training data are labeled. In multiview settings, unlabeled data can be used to regularize the prediction functions, and thus to reduce the search space. In this chapter, we introduce two categories of multiview semi-supervised learning methods. The first one contains the co-training style methods, where the prediction functions from different views are trained through their own objective, and each prediction function is improved by the others. The second one contains the co-regularization style methods, where a single objective function exists for the prediction functions from different views to be trained simultaneously.

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Metadaten
Titel
Multiview Semi-supervised Learning
verfasst von
Shiliang Sun
Liang Mao
Ziang Dong
Lidan Wu
Copyright-Jahr
2019
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-13-3029-2_2