Data Summarization and Distributed Computation

Graham Cormode
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引用次数: 1

Abstract

The notion of summarization is to provide a compact representation of data which approximately captures its essential characteristics. If such summaries can be created, they can lead to efficient distributed algorithms which exchange summaries in order to compute a desired function. In this talk, I'll describe recent efforts in this direction for problems inspired by machine learning: building graphical models over evolving, distributed training examples, and solving robust regression problems over large, distributed data sets.
数据汇总与分布式计算
摘要的概念是提供数据的紧凑表示,以近似地捕捉其基本特征。如果可以创建这样的摘要,它们可以导致高效的分布式算法,这些算法交换摘要以计算所需的函数。在这次演讲中,我将描述最近在这个方向上的努力,以解决由机器学习启发的问题:在不断发展的分布式训练示例上构建图形模型,以及在大型分布式数据集上解决鲁棒回归问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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