Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Support Vector Regression× | K-lähimmät naapurit× | Harjanneregressio× | |
|---|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning | Machine learning |
| Syntyvuosi≠ | 2004 | 1967 | 1970 |
| Kehittäjä≠ | Smola, A.J. & Schölkopf, B. | Cover, T.M. & Hart, P.E. | Hoerl, A.E. & Kennard, R.W. |
| Tyyppi≠ | Kernel-based supervised model (epsilon-insensitive regression) | Instance-based (non-parametric) learning | L2-regularized linear regression |
| Alkuperäislähde≠ | 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 ↗ | Hoerl, A.E. & Kennard, R.W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1), 55–67. DOI ↗ |
| Rinnakkaisnimet | Destek Vektör Regresyonu (SVR), SVR, epsilon-SVR, support vector machine for regression | KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning | Ridge Regresyonu, ridge regresyonu, L2-regularized regression, Tikhonov regularization |
| Liittyvät≠ | 4 | 5 | 4 |
| Tiivistelmä≠ | 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. | 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. |
| ScholarGateAineisto ↗ |
|
|
|