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- W2051279397 abstract "In this paper, information energy metric (IEM) is obtained by similarity computing for high-dimensional samples in a reproducing kernel Hilbert space (RKHS). Firstly, similar/dissimilar subsets and their corresponding informative energy functions are defined. Secondly, IEM is proposed for similarity measure of those subsets, which converts the non-metric distances into metric ones. Finally, applications of this metric is introduced, such as classification problems. Experimental results validate the effectiveness of the proposed method." @default.
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- W2051279397 date "2012-01-01" @default.
- W2051279397 modified "2023-10-17" @default.
- W2051279397 title "INFORMATIVE ENERGY METRIC FOR SIMILARITY MEASURE IN REPRODUCING KERNEL HILBERT SPACES" @default.
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- W2051279397 doi "https://doi.org/10.1080/18756891.2012.670530" @default.
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