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- W2895941612 abstract "A0555. The algorithm put forward in this paper accommodates to both density distance and density of samples' neighborhood distribution. On the one hand, neighbor distance of samples is used to obtain the self-adaptive local distribution parameter to replace the parameter of the heat kernel function. On the other hand, the edge weight of the adjacent matrix is adjusted based on the density difference of sample neighborhood distribution. Simulation results of the UCI dataset and the monitoring dataset obtained by the short-wave receiving system suggest that the algorithm proposed by this paper can more fully dig information of the manifold structure and improve the performance of the Laplacian SVM classifier." @default.
- W2895941612 created "2018-10-26" @default.
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- W2895941612 date "2018-07-01" @default.
- W2895941612 modified "2023-09-27" @default.
- W2895941612 title "Density Adjustment Local Similarity Laplacian SVM (DALS-LapSVM) and its Applications" @default.
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- W2895941612 doi "https://doi.org/10.23919/chicc.2018.8484154" @default.
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