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平面向量值回归中三点加权选择的严格上界

原文标题 · A Strict Upper Bound for Three-Point Weighted Selection in Planar Vector-Valued Regression

作者: Sergey Volkov | 我们证明对于两个输入和两个输出坐标的最小Frobenius范数最小二乘回归,$F_{\mathrm{weighted}}(2,2,3)<11/6$,从而改进了上界$15/8$。更具体地说,一个通用$\varepsilon_0>0$满足对于每一个有限残差框架都有$\mathcal T_3\le(5/6-\varepsilon_0)E$。该证明结合了一个精确的标

来源:arXiv cs.AI/CL/LG · arxiv.org

正文为英文,可一键机器翻译(仅首次需要等待)

Mathematics > Statistics Theory

Title: A Strict Upper Bound for Three-Point Weighted Selection in Planar Vector-Valued Regression

Abstract: We prove $F_{\mathrm{weighted}}(2,2,3)<11/6$ for minimum-Frobenius-norm least-squares regression with two input and two output coordinates, improving the upper bound $15/8$. More precisely, a universal $\varepsilon_0>0$ satisfies $\mathcal T_3\le(5/6-\varepsilon_0)E$ for every finite residual frame. The proof combines an exact scalar certificate, rigidity of equality in planar selection, and a compactness argument that controls disappearing weights. Together with the known lower bound, this gives $13/8\le F_{\mathrm{weighted}}(2,2,3)<11/6$. No numerical value of $\varepsilon_0$ is claimed.

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