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In the medical decision field, the conventional analysis methods of medical insurance data are lack of flexibility and efficiency. When coping with huge and redundant medical dataset, it is difficult to extract the candidate attributes if only using some limited professional knowledge. Therefore, the correlations between the medical insurance cost and relevant factors (e.g. diagnosis results) need...
In this paper, we propose a novel random sampling algorithm for the shortest vector problem (SVP) based on the y-sparse representations of the short lattice vectors. The experimental results show that the random sampling algorithm outperforms the other two SVP algorithms under the benchmarks of SVP challenge[1]. Therefore, the random sampling algorithm is an efficient SVP solver for the shortest vector...
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