Efficient discriminative learning of parametric nearest neighbor classifiers
Linear SVMs are efficient in both training and testing, however the data in real applications is rarely linearly separable. Non-linear kernel SVMs are too computationally intensive for applications with large-scale data sets. Recently locally linear classifiers have gained popularity due to their ef...
| Hoofdauteurs: | , , , , |
|---|---|
| Formaat: | Conference item |
| Taal: | English |
| Gepubliceerd in: |
IEEE
2012
|