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Projected estimators for robust semi-supervised classification
Krijthe, J.H. (author), Loog, M. (author)
For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification using the well-known quadratic surrogate loss function. Unlike other approaches to semi-supervised learning, the procedure proposed in this work does not rely on...
journal article 2017

Source URL (retrieved on 2022-05-28 15:49): https://repository.tudelft.nl/islandora/search/subject%3A%22Least%255C%252Bsquares%255C%252Bclassification%22?f%5B0%5D=mods_subject_topic_ss%3A%22Semi%5C-supervised%5C%20learning%22