首页|Researchers from Texas A&M University Describe Findings in Machine Learning (A Knowledge Transfer Enhanced Ensemble Approach To Predict the Shear C apacity of Reinforced Concrete Deep Beams Without Stirrups)
Researchers from Texas A&M University Describe Findings in Machine Learning (A Knowledge Transfer Enhanced Ensemble Approach To Predict the Shear C apacity of Reinforced Concrete Deep Beams Without Stirrups)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news reporting out of College Station, Texas, by N ewsRx editors, research stated, “This paper proposes a novel learning algorithm, the transfer ensemble neural network (TENN) model, to increase the performance of shear capacity predictions on small datasets, illuminating the usefulness of advanced machine learning techniques in general. By incorporating ensemble learn ing and transfer learning, the TENN model is designed to control the high variab ility inherent in machine learning models trained on small amounts of data.”
College StationTexasUnited StatesN orth and Central AmericaCyborgsEmerging TechnologiesMachine LearningTexa s A&M University
NETL
NSTL
