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Learning Sample Specific Weights for Late Fusion
2015
IEEE Transactions on Image Processing
Late fusion is one of the most effective approaches to enhance recognition accuracy through combining prediction scores of multiple classifiers, each of which is trained by a specific feature or model. The existing methods generally use a fixed fusion weight for one classifier over all samples, and ignore the fact that each classifier may perform better or worse for different subsets of samples. In order to address this issue, we propose a novel sample specific late fusion (SSLF) method.
doi:10.1109/tip.2015.2423560
pmid:25879948
fatcat:pmbu7epunvbtvh24iqtexlcbxq