一种新的压缩计数算法及其在动态数据Shannon熵估计中的应用][A New Algorithm for Compressed Counting with Applications in Shannon Entropy Estimation in Dynamic Data ]

一种新的压缩计数算法及其在动态数据Shannon熵估计中的应用 A New Algorithm for Compressed Counting with Applications in Shannon Entropy Estimation in Dynamic Data


课程网址:	http://videolectures.net/colt2011_li_data/
主讲教师:	Ping Li
开课单位:	康奈尔大学
开课时间:	2011-08-02
课程语种:	英语
中文简介:	本文提出了一种新的压缩计数的精确算法，其样本复杂度仅为O (1/v2)，用于v-加性香农熵估计。这个界的常数因子只有6。此外，我们证明了我们的算法达到了费雪信息的一个上界，事实上它接近100%的统计最优。通过实验验证了算法的正确性。
课程简介:	In this paper, we propose a new accurate algorithm for Compressed Counting, whose sample complexity is only O (1/v2) for v-additive Shannon entropy estimation. The constant factor for this bound is merely about 6. In addition, we prove that our algorithm achieves an upper bound of the Fisher information and in fact it is close to 100% statistically optimal. An empirical study is conducted to verify the accuracy of our algorithm.
关键词:	算术数学; 数学分析; 熵估计
课程来源:	视频讲座网
最后编审:	2019-10-17：cwx
阅读次数:	28

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