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| Support Vector Regression× | Lasso-regression× | Support Vector Machine (Klassifikation)× | |
|---|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning | Machine learning |
| Oprindelsesår≠ | 2004 | 1996 | 1995 |
| Ophavsperson≠ | Smola, A.J. & Schölkopf, B. | Tibshirani, R. | Cortes, C. & Vapnik, V. |
| Type≠ | Kernel-based supervised model (epsilon-insensitive regression) | Regularized linear regression (L1 penalty) | Maximum-margin classifier (kernel method) |
| Oprindelig kilde≠ | Smola, A.J. & Schölkopf, B. (2004). A Tutorial on Support Vector Regression. Statistics and Computing, 14, 199–222. DOI ↗ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Aliasser | Destek Vektör Regresyonu (SVR), SVR, epsilon-SVR, support vector machine for regression | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Relaterede≠ | 4 | 4 | 5 |
| Resumé≠ | 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. | Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter. | 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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