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| Félfelügyelt Támogató Vektor Gép× | Logistic Regression× | |
|---|---|---|
| Tudományterület≠ | Gépi tanulás | Kutatási statisztika |
| Módszercsalád≠ | Machine learning | Process / pipeline |
| Keletkezés éve≠ | 1999 | 1958 |
| Megalkotó≠ | Joachims, T. | David Roxbee Cox |
| Típus≠ | Semi-supervised classifier | Method |
| Alapmű≠ | 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 ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Alternatív nevek≠ | S3VM, Transductive SVM, TSVM, Semi-SVM | logit model, binomial logistic regression, LR |
| Kapcsolódó≠ | 4 | 3 |
| Összefoglaló≠ | 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. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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