השוואת שיטות
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| מכונת וקטורים תומכים בלמידה פעילה× | למידה מונחית-למחצה× | |
|---|---|---|
| תחום | למידת מכונה | למידת מכונה |
| משפחה | Machine learning | Machine learning |
| שנת המקור≠ | 2001 | 1970s–2006 (formalized) |
| הוגה השיטה≠ | Tong, S. & Koller, D. | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| סוג≠ | Active learning + kernel classifier | Learning paradigm |
| מקור מכונן≠ | Tong, S., & Koller, D. (2001). Support Vector Machine Active Learning with Applications to Text Classification. Journal of Machine Learning Research, 2, 45–66. link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| כינויים | Active SVM, AL-SVM, SVM active learning, query-by-committee SVM | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| קשורות≠ | 3 | 5 |
| תקציר≠ | 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. |
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