Reports from Delft University of Technology Describe Recent Advances in Machine Learning (Constrained Infinitesimal Dipole Modeling-assisted Ensemble Prediction of Embedded Element Patterns Via Machine Learning)

Delft理工大学的报告描述了机器学习的最新进展(通过机器学习的受限无限小偶极子模型辅助集成预测嵌入元素模式)

Robotics & Machine Learning Daily News2024，Issue(Dec.4) ：170-171.

来源：

NETL
NSTL

Reports from Delft University of Technology Describe Recent Advances in Machine Learning (Constrained Infinitesimal Dipole Modeling-assisted Ensemble Prediction of Embedded Element Patterns Via Machine Learning)

Delft理工大学的报告描述了机器学习的最新进展(通过机器学习的受限无限小偶极子模型辅助集成预测嵌入元素模式)

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每日新闻-调查人员讨论机器学习的新发现。根据新闻报道在荷兰代尔夫特，由News Rx编辑进行的研究表明，“一种新的集合预测技术是引入d提高互作用下远场嵌入元模式(EEP)预测精度基于神经网络(nn)的训练数据规模优化耦合(MC)效应算法。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators discuss new findings in Machine Learning. According to news reportingout of Delft, Netherlands, by News Rx editors, research stated, “A novel ensemble prediction technique isintroduce d to enhance the accuracy of far-field embedded element pattern (EEP) prediction under mutualcoupling (MC) effects, while relaxing the training data size chall enge in neural network (NN)-basedalgorithms.”

Key words

Delft/Netherlands/Europe/Cyborgs/Eme rging Technologies/Machine Learning/Delft University of Technology

引用本文复制引用

出版年

2024

Robotics & Machine Learning Daily News

ISSN：

段落导航