Efficient discriminative learning of parametric nearest neighbor classifiers

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...

书目详细资料
Main Authors:	Zhang, Z, Sturgess, P, Sengupta, S, Crook, N, Torr, PHS
格式:	Conference item
语言:	English
出版:	IEEE 2012

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