首页|New Findings in Machine Learning Described from HeNan Polytechnic University (Re construction of Geodetic Time Series With Missing Data and Time-varying Seasonal Signals Using Gaussian Process for Machine Learning)
New Findings in Machine Learning Described from HeNan Polytechnic University (Re construction of Geodetic Time Series With Missing Data and Time-varying Seasonal Signals Using Gaussian Process for Machine Learning)
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-Research findings on Machine Learning are discussed in a new report. According to news reporting from Jiaozuo,People' s Republic of China,by NewsRx journalists,research stated,"Seasonal signals i n satellite geodesy time series are mainly derived from a number of loading sour ces,such as atmospheric pressure and hydrological loading. The most common meth od for modeling the seasonal signal with quasi-period is to use the sine and cos ine functions with the constant amplitude for approximation." Financial supporters for this research include National Natural Science Foundati on of China (NSFC),National Natural Science Foundation of China (NSFC).
JiaozuoPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesGaussian ProcessesMachine LearningHeNan P olytechnic University
NETL
NSTL
