A Taxonomy for Semi-Supervised Learning Methods
作者： Seeger Matthias
DOI: 10.7551/MITPRESS/9780262033589.003.0002
关键词:
摘要: We propose a simple taxonomy of probabilistic graphical models for the semi-supervised learning problem. give some broad classes algorithms each families and point to specific realizations in literature. Finally, we shed more detailed light on family methods using input-dependent regularization (or conditional prior distributions) show parallels Co-training paradigm.
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sci-hub.st 参考文章(20)
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