পদ্ধতির তুলনা করুন

	অ্যাক্টিভ লার্নিং লজিস্টিক রিগ্রেশন	আধা-তত্ত্বাবধানাধীন শিখন (Semi-supervised Learning)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1994–2010	1970s–2006 (formalized)
প্রবর্তক	Lewis, D. D. & Gale, W. A.; Settles, B. (survey)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
ধরন	Active learning framework with logistic regression base learner	Learning paradigm
মৌলিক উৎস	Settles, B. (2010). Active Learning Literature Survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
অপর নাম	AL-LR, logistic regression active learner, uncertainty sampling logistic regression, pool-based active logistic classifier	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
সম্পর্কিত	4	5
সারসংক্ষেপ	Active Learning with Logistic Regression is an iterative label-efficient framework in which a logistic regression model selects the unlabeled examples it is most uncertain about, an oracle (human annotator) labels them, and the model is retrained — repeating until a labeling budget or accuracy target is met. It dramatically reduces annotation cost compared to random labeling.	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.