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...
| Autori principali: | , , , , |
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| Natura: | Conference item |
| Lingua: | English |
| Pubblicazione: |
IEEE
2012
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