Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| K-Nearest Neighbors× | Ridge-regressie× | Support Vector Machine (Classificatie)× | |
|---|---|---|---|
| Vakgebied | Machine learning | Machine learning | Machine learning |
| Familie | Machine learning | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 1967 | 1970 | 1995 |
| Grondlegger≠ | Cover, T.M. & Hart, P.E. | Hoerl, A.E. & Kennard, R.W. | Cortes, C. & Vapnik, V. |
| Type≠ | Instance-based (non-parametric) learning | L2-regularized linear regression | Maximum-margin classifier (kernel method) |
| Oorspronkelijke bron≠ | Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27. DOI ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Aliassen | KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Verwant≠ | 5 | 4 | 5 |
| Samenvatting≠ | 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. | Ridge Regression is an L2-regularized linear regression method, introduced by Arthur Hoerl and Robert Kennard in 1970, that reduces multicollinearity by adding a penalty on the size of the coefficients. It shrinks coefficients toward zero without setting any of them exactly to zero, producing more stable estimates when predictors are highly correlated. | 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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