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English(EN) On the Triangle Inequality for the Jaccard Distance in Arbitrary Lattices

格和估值的新Jaccard距离理论

本文探讨了将Jaccard距离推广到格和估值的理论进展。研究表明,在特定条件下,例如严格正、单调和模估值,Jaccard距离在任意格上满足三角不等式。研究进一步探讨了这些性质在互补分配格中的应用,并确定了超模态性是标准广义Jaccard距离作为有效度量函数运行的严格要求。研究结果已应用于量子信息论、形式概念分析和machine learning等领域。 AI

影响 为可能应用于machine learning算法的距离度量提供了理论基础。

排序理由 该条目是一篇学术论文,详细介绍了具有machine learning应用前景的数学理论结果。[lever_c_demoted from research: ic=1 ai=0.7]

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格和估值的新Jaccard距离理论

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该条目是一篇学术论文,详细介绍了具有machine learning应用前景的数学理论结果。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Costin B\u{a}dic\u{a}, Amelia B\u{a}dic\u{a} ·

    关于任意格中Jaccard距离的三角不等式

    arXiv:2608.18194v1 Announce Type: new Abstract: This paper presents new theoretical results on generalizing the Jaccard distance for lattices and real valuations. We demonstrate that when the valuation is strictly positive, monotone, and modular, the Jaccard distance satisfies th…