Methoden vergleichen

	Active Learning Support Vector Machine	Support Vector Machine (Klassifikation)
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2001	1995
Urheber	Tong, S. & Koller, D.	Cortes, C. & Vapnik, V.
Typ	Active learning + kernel classifier	Maximum-margin classifier (kernel method)
Wegweisende Quelle	Tong, S., & Koller, D. (2001). Support Vector Machine Active Learning with Applications to Text Classification. Journal of Machine Learning Research, 2, 45–66.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Aliasnamen	Active SVM, AL-SVM, SVM active learning, query-by-committee SVM	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Verwandt	3	5
Zusammenfassung	Active learning SVM combines the strong decision-boundary of support vector machines with an intelligent query strategy that selects the most informative unlabeled instances for human annotation. Introduced by Tong and Koller in 2001, it achieves high classification accuracy using far fewer labeled examples than passive supervised learning, making it practical whenever labeling is expensive or slow.	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.