Sammenlign metoder

	Semi-superviseret K-Nærmeste Naboer	Semi-supervised Support Vector Machine
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2002 (semi-supervised extension); 1967 (KNN base)	1999
Ophavsperson	Zhu, X. & Ghahramani, Z. (label propagation); Cover, T. & Hart, P. (KNN base)	Joachims, T.
Type	Semi-supervised classifier / label propagation	Semi-supervised classifier
Oprindelig kilde	Zhu, X. & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.	Joachims, T. (1999). Transductive Inference for Text Classification using Support Vector Machines. Proceedings of the 16th International Conference on Machine Learning (ICML), 200–209.
Aliasser	SS-KNN, semi-supervised KNN, KNN label propagation, graph-based semi-supervised KNN	S3VM, Transductive SVM, TSVM, Semi-SVM
Relaterede	4	4
Resumé	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 Support Vector Machine (S3VM) extends the classical SVM by incorporating large quantities of unlabeled data alongside a small labeled training set. It seeks a maximum-margin hyperplane that not only separates the labeled examples but also passes through low-density regions of the full data distribution, yielding better generalization when labeled samples are scarce.