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| Félfelügyelt Gausszi-folyam× | Félfelügyelt Támogató Vektor Gép× | |
|---|---|---|
| Tudományterület | Gépi tanulás | Gépi tanulás |
| Módszercsalád | Machine learning | Machine learning |
| Keletkezés éve≠ | 2004 | 1999 |
| Megalkotó≠ | Lawrence, N. D. & Jordan, M. I. | Joachims, T. |
| Típus≠ | Probabilistic model (semi-supervised) | Semi-supervised classifier |
| Alapmű≠ | Lawrence, N. D., & Jordan, M. I. (2004). Semi-supervised learning via Gaussian processes. In Advances in Neural Information Processing Systems (NIPS), 17, 753–760. MIT Press. link ↗ | Joachims, T. (1999). Transductive Inference for Text Classification using Support Vector Machines. Proceedings of the 16th International Conference on Machine Learning (ICML), 200–209. link ↗ |
| Alternatív nevek | SS-GP, semi-supervised GP, Gaussian process with unlabeled data, GP manifold learning | S3VM, Transductive SVM, TSVM, Semi-SVM |
| Kapcsolódó≠ | 5 | 4 |
| Összefoglaló≠ | Semi-supervised Gaussian Process extends the probabilistic GP framework to exploit unlabeled data alongside a small set of labeled observations. By placing a GP prior over functions and leveraging the geometric structure revealed by unlabeled inputs, it learns more accurate and better-calibrated predictors than a purely supervised GP when labels are scarce, making it well suited for scientific and medical problems where annotation is expensive. | Semi-supervised Support Vector Machine (S3VM) extends the classical SVM by incorporating large quantities of unlabeled data alongside a small labeled training set. It seeks a maximum-margin hyperplane that not only separates the labeled examples but also passes through low-density regions of the full data distribution, yielding better generalization when labeled samples are scarce. |
| ScholarGateAdatkészlet ↗ |
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