Robotics & Machine Learning Daily News2024,Issue(Dec.3) :56-57.

Data from Charles University of Prague Provide New Insights into Machine Learnin g (Learnability of State Spaces of Physical Systems Is Undecidable)

布拉格查尔斯大学的数据为机器学习提供了新的见解(物理系统状态空间的可学习性是无法判定的)

Robotics & Machine Learning Daily News2024,Issue(Dec.3) :56-57.

Data from Charles University of Prague Provide New Insights into Machine Learnin g (Learnability of State Spaces of Physical Systems Is Undecidable)

布拉格查尔斯大学的数据为机器学习提供了新的见解(物理系统状态空间的可学习性是无法判定的)

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摘要

由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-研究人员详细介绍机器学习的新数据。根据新闻报道来自捷克共和国布拉格的NewsRx记者的研究表明,“尽管越来越多的人关于机器学习在科学中的作用,由于受限于由机器辅助的经验探索,目前还缺乏结果机器学习。本文通过构造可学习性的不可判定性,构造了这样一个极限物理系统的状态空间"。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Researchers detail new data in Machine Learning. According to news reporting originatingfrom Prague, Czech Republic, by NewsRx correspondents, research stated, “Despite an increasingrole of machin e learning in science, there is a lack of results on limits of empirical explora tion aided bymachine learning. In this paper, we construct one such limit by pr oving undecidability of learnability ofstate spaces of physical systems.”

Key words

Prague/Czech Republic/Europe/Cyborgs/Emerging Technologies/Machine Learning/Charles University of Prague

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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