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-一项关于机器学习的新研究现在可用。根据来自…的消息中华人民共和国上海，NewsRx通讯员，研究报告，“机器的应用”学习(ML)预测砂浆粘结强度有助于降低试验成本，提高预测精度。本研究探讨四种ML模型-反向投影神经网络(BPNN),支持向量回归(SVR)、随机森林(RF)和极端梯度Boosting(xgboost)-使用304个实验数据点进行训练，76个结果进行测试。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – A new study on Machine Learning is now available. According to news originating fromShanghai, People’s Republic of Ch ina, by NewsRx correspondents, research stated, “Application of machinelearning (ML) in predicting mortars bond strength contributes to low experimental cost a nd high accuracy.This study explores the performance of four ML models-Back Pro pagation Neural Network (BPNN),Support Vector Regression (SVR), Random Forest ( RF), and eXtreme Gradient Boosting (XGBoost)-using 304 experimental data points for training and 76 results for testing.”
NETL
NSTL