机器学习模型构建和数据挖掘算法执行的进展指标:立场文件。

Gang Luo
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引用次数: 16

摘要

为了方便用户,许多软件系统为长时间的任务提供进度指示器。典型的进度指示器持续估计任务的剩余执行时间以及任务已完成的部分。构建机器学习模型通常需要很长时间,但现有的机器学习软件没有提供一个重要的进度指标。类似地,运行数据挖掘算法通常需要很长时间,但是没有现有的数据挖掘软件提供重要的进度指示器。在本文中,我们考虑了为机器学习模型构建和数据挖掘算法执行提供进度指标的问题。我们将讨论这个问题的内在目标和挑战。然后,我们描述了实施这些进展指标的初步框架以及它们的两个先进的潜在用途,目的是启发未来对这一主题的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Toward a Progress Indicator for Machine Learning Model Building and Data Mining Algorithm Execution: A Position Paper.

Toward a Progress Indicator for Machine Learning Model Building and Data Mining Algorithm Execution: A Position Paper.

Toward a Progress Indicator for Machine Learning Model Building and Data Mining Algorithm Execution: A Position Paper.

Toward a Progress Indicator for Machine Learning Model Building and Data Mining Algorithm Execution: A Position Paper.

For user-friendliness, many software systems offer progress indicators for long-duration tasks. A typical progress indicator continuously estimates the remaining task execution time as well as the portion of the task that has been finished. Building a machine learning model often takes a long time, but no existing machine learning software supplies a non-trivial progress indicator. Similarly, running a data mining algorithm often takes a long time, but no existing data mining software provides a nontrivial progress indicator. In this article, we consider the problem of offering progress indicators for machine learning model building and data mining algorithm execution. We discuss the goals and challenges intrinsic to this problem. Then we describe an initial framework for implementing such progress indicators and two advanced, potential uses of them, with the goal of inspiring future research on this topic.

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