In this paper , inspired by KNN-SVM ( Zhang et al. , 2006 ) , we propose a local training method , which trains sentence-wise weights instead of a single weight , to address the above two problems .
svmknn discriminative nearest neighbor classification for visual category recognition
Hao Zhang Alexander C Berg Michael Maire Jitendra Malik
However , we can achieve this with two weights : ( 1 , 1 ) for f1 and ( â 1 , 1 ) for f2 . In this paper , inspired by KNN-SVM ( Zhang et al. , 2006 ) , we propose a local training method , which trains sentence-wise weights instead of a single weight , to address the above two problems . Compared with global training methods , such as MERT , in which training and testing are separated , our method works in an online fashion , in which training is performed during testing .
In this paper , inspired by KNN-SVM ( @@CITATION ) , we propose a local training method , which trains sentence-wise weights instead of a single weight , to address the above two problems .