Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Pusgadīgi uzraudzīta viena klases SVM× | Daudzpusīgā apguve× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2001–2004 | 1970s–2006 (formalized) |
| Autors≠ | Extension of Scholkopf et al. (2001); semi-supervised variants studied ca. 2004–2010 | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Tips≠ | Semi-supervised anomaly / novelty detection | Learning paradigm |
| Pirmavots≠ | Munoz, A. & Muruzabal, J. (2004). Self-Organising Maps for Outlier Detection. Neurocomputing, 58–60, 953–956. link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Citi nosaukumi | SS-OCSVM, semi-supervised OC-SVM, semi-supervised novelty detection SVM, transductive one-class SVM | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Semi-supervised One-class SVM extends the classic One-class SVM anomaly detector by incorporating unlabeled observations alongside a small set of known normal examples. The unlabeled data helps the model learn a tighter, more informative decision boundary in feature space, reducing false positives and improving anomaly recall compared to the purely unsupervised baseline. | 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. |
| ScholarGateDatu kopa ↗ |
|
|