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...

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Chi tiết về thư mục
Những tác giả chính:	Zhang, Z, Sturgess, P, Sengupta, S, Crook, N, Torr, PHS
Định dạng:	Conference item
Ngôn ngữ:	English
Được phát hành:	IEEE 2012

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