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Regularized semi-supervised learning

Regularized semi-supervised learning adds explicit geometric or graph-based penalty terms to a semi-supervised objective so that the decision function varies smoothly over the data manifold. Pioneered through manifold regularization (Belkin, Niyogi & Sindhwani, 2006), it exploits the structure of both labeled and unlabeled examples to learn more accurate models than supervised regularization alone when labeled data are scarce.

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Regularized Semi-Supervised Learning (Manifold Regularization and Graph-Based SSL)

Taksonomski zapis metode · ml-model / machine-learning

Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7, 2399–2434. · URL
Chapelle, O., Scholkopf, B., & Zien, A. (Eds.). (2006). Semi-Supervised Learning. MIT Press. · ISBN 978-0-262-03358-9

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Taxonomic bucketGaussian Processmachine-suggested · Relational suggestion, not evidence.Same method familyLabel Propagationmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRegularized Logistic Regressionmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRegularized random forestmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSelf-supervised Learningmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSemi-supervised Learningmachine-suggested · Relational suggestion, not evidence.