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...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Z, Sturgess, P, Sengupta, S, Crook, N, Torr, PHS
Formato: Conference item
Lenguaje:English
Publicado: IEEE 2012