Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Régression par vecteurs de support× | Plus Proches Voisins (PPV)× | Machine à vecteurs de support (Classification)× | |
|---|---|---|---|
| Domaine | Apprentissage automatique | Apprentissage automatique | Apprentissage automatique |
| Famille | Machine learning | Machine learning | Machine learning |
| Année d'origine≠ | 2004 | 1967 | 1995 |
| Auteur d'origine≠ | Smola, A.J. & Schölkopf, B. | Cover, T.M. & Hart, P.E. | Cortes, C. & Vapnik, V. |
| Type≠ | Kernel-based supervised model (epsilon-insensitive regression) | Instance-based (non-parametric) learning | Maximum-margin classifier (kernel method) |
| Source fondatrice≠ | Smola, A.J. & Schölkopf, B. (2004). A Tutorial on Support Vector Regression. Statistics and Computing, 14, 199–222. DOI ↗ | Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27. DOI ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Alias | Destek Vektör Regresyonu (SVR), SVR, epsilon-SVR, support vector machine for regression | KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Apparentées≠ | 4 | 5 | 5 |
| Résumé≠ | Support Vector Regression (SVR), described in Smola and Schölkopf's 2004 tutorial, predicts a continuous outcome by fitting a function that stays within an epsilon-wide tube around the data while incurring as little error as possible. It extends the support vector machine idea from classification to regression, using a kernel to capture nonlinear relationships. | 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. | 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. |
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