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| Pooljärelevalvega meetrika õppimine× | Poolitatud järelevalvega õppimine× | |
|---|---|---|
| Valdkond | Masinõpe | Masinõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2007–2008 | 1970s–2006 (formalized) |
| Looja≠ | Yeung, D.-Y. & Chang, H.; Davis, J. V. & Dhillon, I. S. | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Tüüp≠ | Hybrid supervised/unsupervised distance learning | Learning paradigm |
| Algallikas≠ | Yeung, D.-Y., & Chang, H. (2007). A kernel approach for semi-supervised metric learning. IEEE Transactions on Neural Networks, 18(1), 141–149. DOI ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Rööpnimetused | SSML, semi-supervised distance learning, constrained metric learning, weakly supervised metric learning | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Seotud | 5 | 5 |
| Kokkuvõte≠ | Semi-supervised metric learning learns a task-adapted distance function by combining a small set of labeled pairwise constraints — must-link and cannot-link pairs — with the geometric structure of a much larger pool of unlabeled data. The result is a Mahalanobis-style or kernel-based distance that reflects both supervision and data topology, improving downstream tasks such as nearest-neighbor classification and clustering. | 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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