পদ্ধতির তুলনা করুন

	সক্রিয় শিখন সাপোর্ট ভেক্টর মেশিন	আধা-তত্ত্বাবধানাধীন শিখন (Semi-supervised Learning)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2001	1970s–2006 (formalized)
প্রবর্তক	Tong, S. & Koller, D.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
ধরন	Active learning + kernel classifier	Learning paradigm
মৌলিক উৎস	Tong, S., & Koller, D. (2001). Support Vector Machine Active Learning with Applications to Text Classification. Journal of Machine Learning Research, 2, 45–66.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
অপর নাম	Active SVM, AL-SVM, SVM active learning, query-by-committee SVM	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
সম্পর্কিত	3	5
সারসংক্ষেপ	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.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.