Salīdzināt metodes

	Pašuzraudzīta k-tuvāko kaimiņu metode	Pusgadīgi K tuvāko kaimiņu metode
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2018–2020	2002 (semi-supervised extension); 1967 (KNN base)
Autors	Wu, Z. et al. / Chen, T. et al.	Zhu, X. & Ghahramani, Z. (label propagation); Cover, T. & Hart, P. (KNN base)
Tips	Self-supervised + non-parametric classifier	Semi-supervised classifier / label propagation
Pirmavots	Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119, 1597–1607.	Zhu, X. & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Citi nosaukumi	SSL-kNN, self-supervised kNN classifier, kNN evaluation probe, nearest-neighbor self-supervised classifier	SS-KNN, semi-supervised KNN, KNN label propagation, graph-based semi-supervised KNN
Saistītās	4	4
Kopsavilkums	Self-supervised K-nearest neighbors (SSL-kNN) combines representation learning without labels with a non-parametric k-NN classifier. A neural encoder is first trained via a self-supervised objective — such as contrastive or masked prediction — so that semantically similar samples cluster together in the embedding space. A simple k-NN lookup on those embeddings then assigns class labels, serving both as a lightweight probe and as a practical classifier.	Semi-supervised KNN extends the classic K-nearest neighbors algorithm to exploit large pools of unlabeled data alongside a small labeled set. By building a KNN graph over all observations and propagating known labels through the graph's edges, the method infers labels for unlabeled points without requiring expensive manual annotation of every sample.