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-研究人员详细介绍机器学习的新数据。根据新闻报道密苏里州罗拉，由NewsRx编辑，研究指出：“这项研究引入了一种新的方法来预测基于可解释机器学习的FRP-混凝土界面抗剪承载力研究。八种算法采用：三个独立模型（人工神经网络k、支持向量回归和决策树)和五种集成学习模式LS(Bagging、Random Forest、Adaptive Boosting、梯度Boosting、和极端的Gr Adent Boosting)。
Abstract
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 out ofRolla, Missouri, by NewsRx editors , research stated, “This study introduces a novel approach to predict theshear bearing capacity of FRP-concrete interfaces using explainable machine learning. Eight algorithms areemployed: three standalone models (Artificial Neural Networ k, Support Vector Regression, and DecisionTree) and five ensemble learning mode ls (Bagging, Random Forest, Adaptive Boosting, Gradient Boosting,and Extreme Gr adient Boosting).”
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