Salīdzināt metodes

	Pusgadīgi K tuvāko kaimiņu metode	Daļēji uzraudzīts Gausa process
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2002 (semi-supervised extension); 1967 (KNN base)	2004
Autors	Zhu, X. & Ghahramani, Z. (label propagation); Cover, T. & Hart, P. (KNN base)	Lawrence, N. D. & Jordan, M. I.
Tips	Semi-supervised classifier / label propagation	Probabilistic model (semi-supervised)
Pirmavots	Zhu, X. & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.	Lawrence, N. D., & Jordan, M. I. (2004). Semi-supervised learning via Gaussian processes. In Advances in Neural Information Processing Systems (NIPS), 17, 753–760. MIT Press.
Citi nosaukumi	SS-KNN, semi-supervised KNN, KNN label propagation, graph-based semi-supervised KNN	SS-GP, semi-supervised GP, Gaussian process with unlabeled data, GP manifold learning
Saistītās	4	5
Kopsavilkums	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.	Semi-supervised Gaussian Process extends the probabilistic GP framework to exploit unlabeled data alongside a small set of labeled observations. By placing a GP prior over functions and leveraging the geometric structure revealed by unlabeled inputs, it learns more accurate and better-calibrated predictors than a purely supervised GP when labels are scarce, making it well suited for scientific and medical problems where annotation is expensive.