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- W2344657534 abstract "Maximum entropy discrimination (MED) is a general framework for discriminative estimation which integrates the principles of maximum entropy and maximum margin. In this paper, we propose a novel approach named multi-kernel MED (MKMED) for multi-view learning (MVL), which takes advantage of the comp lementary principle for MVL. Multiple kernels encode the similarities in different views. We obtain a kernel matrix by multiple kernel combination to make use of the complementary information in different views. Based on the kernel matrix obtained by multiple kernel combination, we can proceed MVL within the MED framework. The experimental results on multiple datasets demonstrate the effectiveness of the proposed MKMED. MKMED outperforms the single-view MEDs and a competing MVL mothod named SVM-2K, and is competitive with the state-of-the-art multi-view MED (MVMED) and even sometimes exceeds it." @default.
- W2344657534 created "2016-06-24" @default.
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- W2344657534 date "2016-04-20" @default.
- W2344657534 modified "2023-10-16" @default.
- W2344657534 title "Multi-kernel maximum entropy discrimination for multi-view learning" @default.
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- W2344657534 doi "https://doi.org/10.3233/ida-160816" @default.
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