首页|Data from Charles University of Prague Provide New Insights into Machine Learnin g (Learnability of State Spaces of Physical Systems Is Undecidable)
Data from Charles University of Prague Provide New Insights into Machine Learnin g (Learnability of State Spaces of Physical Systems Is Undecidable)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
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.”
PragueCzech RepublicEuropeCyborgsEmerging TechnologiesMachine LearningCharles University of Prague
NETL
NSTL
