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• W2254134911 abstract "Kernel methods provide a convenient way to apply a wide range of learning techniques to complex and structured data by shifting the representational problem from one of finding an embedding of the data to that of defining a positive semi-definite kernel. One problem with the most widely used kernels is that they neglect the locational information within the structures, resulting in less discrimination. Correspondence-based kernels, on the other hand, are in general more discriminating, at the cost of sacrificing positive-definiteness due to their inability to guarantee transitivity of the correspondences between multiple graphs. In this paper we adopt a general framework for the projection of (relaxed) correspondences onto the space of transitive correspondences, thus transforming any given matching algorithm onto a transitive multi-graph matching approach. The resulting transitive correspondences can then be used to provide a kernel that both maintains locational information and is guaranteed to be positive-definite. Experimental evaluation validates the effectiveness of the kernel for several structural classification tasks." @default.
• W2254134911 created "2016-06-24" @default.
• W2254134911 creator A5004269930 @default.
• W2254134911 creator A5029508185 @default.
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• W2254134911 date "2015-01-01" @default.
• W2254134911 modified "2023-09-25" @default.
• W2254134911 title "Transitive Assignment Kernels for Structural Classification" @default.
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• W2254134911 doi "https://doi.org/10.1007/978-3-319-24261-3_12" @default.
• W2254134911 hasPublicationYear "2015" @default.
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