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| Félfelügyelt autoencoderes anomáliadetektálás× | Félfelügyelt tanulás× | |
|---|---|---|
| Tudományterület | Gépi tanulás | Gépi tanulás |
| Módszercsalád | Machine learning | Machine learning |
| Keletkezés éve≠ | 2018–2020 | 1970s–2006 (formalized) |
| Megalkotó≠ | Ruff, L. et al.; Zong, B. et al. | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Típus≠ | Semi-supervised deep anomaly detection | Learning paradigm |
| Alapmű≠ | Ruff, L., Vandermeulen, R. A., Franks, B. J., Müller, K.-R., & Kloft, M. (2020). Deep Semi-Supervised Anomaly Detection. In International Conference on Learning Representations (ICLR 2020). link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Alternatív nevek | Semi-supervised AE anomaly detection, SSAD autoencoder, semi-supervised reconstruction-error detection, partially labeled autoencoder anomaly detection | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Kapcsolódó | 5 | 5 |
| Összefoglaló≠ | Semi-supervised Autoencoder Anomaly Detection trains a neural autoencoder primarily on normal (unlabeled) data, then uses a small set of labeled anomalies to refine decision boundaries, detecting anomalies as samples with high reconstruction error. It bridges the gap between purely unsupervised autoencoders and fully supervised classifiers when labels are scarce but some known anomalies exist. | 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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