论文标题
改善近似自我改善的分散器和其估计熵的误差分析
Improvement of an Approximated Self-Improving Sorter and Error Analysis of its Estimated Entropy
论文作者
论文摘要
Ailon等人提出的自我改进魔法器。由两个阶段组成：一个相对较长的训练阶段和快速操作阶段。在这项研究中，我们开发了一种有效的方法来通过近似其训练阶段更快，但在操作阶段不牺牲太多的性能来进一步改善该分散者。当我们测试该近似分类器的性能时，必须确保估计熵的准确性。因此，我们进一步开发了一个有用的公式，以计算从具有未知分布的输入数据得出的估计熵的“误差”上限。我们的工作将有助于更好地利用此自我改进的分散器以更快的方式对大型数据进行巨大的数据。
The self-improving sorter proposed by Ailon et al. consists of two phases: a relatively long training phase and rapid operation phase. In this study, we have developed an efficient way to further improve this sorter by approximating its training phase to be faster but not sacrificing much performance in the operation phase. It is very necessary to ensure the accuracy of the estimated entropy when we test the performance of this approximated sorter. Thus we further developed a useful formula to calculate an upper bound for the 'error' of the estimated entropy derived from the input data with unknown distributions. Our work will contribute to the better use of this self-improving sorter for huge data in a quicker way.
