Researchers from University of New South Wales Report Recent Findings in Machine Learning (Smoothing and Approximation of Grassland Fire Loading Data for Engine ering Structures By Capped Extended Support Vector Regression)

来自新南威尔士大学的研究人员报告了机器学习的最新发现(通过上限扩展支持向量回归平滑和近似发动机工程结构的草原火灾载荷数据)

Robotics & Machine Learning Daily News2024，Issue(Nov.15) ：54-54.

来源：

NETL
NSTL

Researchers from University of New South Wales Report Recent Findings in Machine Learning (Smoothing and Approximation of Grassland Fire Loading Data for Engine ering Structures By Capped Extended Support Vector Regression)

来自新南威尔士大学的研究人员报告了机器学习的最新发现(通过上限扩展支持向量回归平滑和近似发动机工程结构的草原火灾载荷数据)

扫码查看

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一项关于机器学习的新研究现已问世。据悉尼的新闻报道，澳大利亚，NewsRx记者，Rese Arch说，“这项研究提出了一个机器学习辅助数据。”工程结构草原火荷载调查的平滑近似方法。草原火荷载的调查对野外防火设计、建筑规范、建筑规范等有重要影响特定地区的法规等。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – A new study on Machine Learning is now available. According to news reporting from Sydney,Australia, by NewsRx journalists, rese arch stated, “This research proposes a machine learning-aided datasmoothing and approximation scheme to investigate grassland fire loading for engineering stru ctures. Theinvestigations on grassland fire loading have significant impacts on wildfire-resistant design, building codes,regulations, and the like in specifi c regions.”

Key words

Sydney/Australia/Australia and New Zea land/Algorithms/Cyborgs/Emerging Technologies/Engineering/Machine Learning/Support Vector Regression/University of New South Wales

引用本文复制引用

出版年

2024

Robotics & Machine Learning Daily News

ISSN：

段落导航