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| 서포트 벡터 회귀× | 서포트 벡터 머신 (분류)× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2004 | 1995 |
| 창시자≠ | Smola, A.J. & Schölkopf, B. | Cortes, C. & Vapnik, V. |
| 유형≠ | Kernel-based supervised model (epsilon-insensitive regression) | Maximum-margin classifier (kernel method) |
| 원전≠ | Smola, A.J. & Schölkopf, B. (2004). A Tutorial on Support Vector Regression. Statistics and Computing, 14, 199–222. DOI ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| 별칭 | Destek Vektör Regresyonu (SVR), SVR, epsilon-SVR, support vector machine for regression | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| 관련≠ | 4 | 5 |
| 요약≠ | 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. | 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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