পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| সক্রিয় শিখন (Active Learning)× | আধা-তত্ত্বাবধানাধীন শিখন (Semi-supervised Learning)× | |
|---|---|---|
| ক্ষেত্র | যন্ত্র শিখন | যন্ত্র শিখন |
| পরিবার | Machine learning | Machine learning |
| উদ্ভবের বছর≠ | 2009 | 1970s–2006 (formalized) |
| প্রবর্তক≠ | Burr Settles | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| ধরন≠ | Interactive supervised learning framework | Learning paradigm |
| মৌলিক উৎস≠ | Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648. link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| অপর নাম | Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| সম্পর্কিত≠ | 2 | 5 |
| সারসংক্ষেপ≠ | Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottleneck of annotation cost by achieving high model accuracy with far fewer labeled examples than passive supervised learning requires. | 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. |
| ScholarGateডেটাসেট ↗ |
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