Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Методът на опорните вектори (класификация)× | К-най-близки съседи× | Логистична регресия× | |
|---|---|---|---|
| Област≠ | Машинно обучение | Машинно обучение | Статистика за изследвания |
| Семейство≠ | Machine learning | Machine learning | Process / pipeline |
| Година на възникване≠ | 1995 | 1967 | 1958 |
| Създател≠ | Cortes, C. & Vapnik, V. | Cover, T.M. & Hart, P.E. | David Roxbee Cox |
| Тип≠ | Maximum-margin classifier (kernel method) | Instance-based (non-parametric) learning | Method |
| Основополагащ източник≠ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ | Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Други названия≠ | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier | KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning | logit model, binomial logistic regression, LR |
| Свързани≠ | 5 | 5 | 3 |
| Резюме≠ | The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data. | K-Nearest Neighbors (KNN), formalized by Cover and Hart in 1967, is a non-parametric, instance-based method that classifies or predicts a new observation by looking at the k closest examples in the training data. For classification it takes a majority vote among those neighbors; for regression it averages their values. | 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. |
| ScholarGateНабор от данни ↗ |
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