摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了关于机器学习的新报告。根据NewsRx编辑对北京的新闻报道，研究表明：“本文采用支持向量回归(SVR)、随机森林回归(RF)、梯度Boosting(GB)和极值梯度t Boosting(XGB)算法，建立了再生混凝土(RAC)抗压强度预测模型，分析了10个输入s对再生混凝土抗压强度的影响，并结合最佳预测模型。”将非支配排序遗传算法II(NSGA-II)应用于RAC混合比例的多目标优化，以成本、碳排放和D抗压强度为关键目标。本研究的资助单位包括国家自然科学基金、中铁集团有限公司铁路基础研究联合基金项目、北京自然科学基金。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on Machine Learn ing. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “This paper employs Support Vector Regression ( SVR), Random Forest Regression (RF), Gradient Boosting (GB), and Extreme Gradien t Boosting (XGB) algorithms to establish the compressive strength prediction mod els for Recycled Aggregate Concrete (RAC) and analyze the influence of ten input s on RAC compressive strength. Combined with the best prediction model, the Non- dominated Sorting Genetic Algorithm II (NSGA-II) is applied for multiobjective optimization of mixture proportions in RAC addressing cost, carbon emissions, an d compressive strength as key objectives.” Financial supporters for this research include National Natural Science Foundati on of China-China National Railway Group Co., Ltd. Railway Basic Research Joint Fund Project, Beijing Natural Science Foundation.
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