Jämför metoder

	Semi-supervised Support Vector Machine	Support Vector Machine (Klassificering)
Ämnesområde	Maskininlärning	Maskininlärning
Familj	Machine learning	Machine learning
Ursprungsår	1999	1995
Upphovsperson	Joachims, T.	Cortes, C. & Vapnik, V.
Typ	Semi-supervised classifier	Maximum-margin classifier (kernel method)
Ursprungskälla	Joachims, T. (1999). Transductive Inference for Text Classification using Support Vector Machines. Proceedings of the 16th International Conference on Machine Learning (ICML), 200–209.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Alias	S3VM, Transductive SVM, TSVM, Semi-SVM	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Närliggande	4	5
Sammanfattning	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.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.