Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Regression ya Vigawe Tezi× | Jirani-Karibu-Wengi× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Mashine | Ujifunzaji wa Mashine |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2004 | 1967 |
| Mwanzilishi≠ | Smola, A.J. & Schölkopf, B. | Cover, T.M. & Hart, P.E. |
| Aina≠ | Kernel-based supervised model (epsilon-insensitive regression) | Instance-based (non-parametric) learning |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala | Destek Vektör Regresyonu (SVR), SVR, epsilon-SVR, support vector machine for regression | KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning |
| Zinazohusiana≠ | 4 | 5 |
| Muhtasari≠ | 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. |
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