首页|Study Results from Universitas Ahmad Dahlan Broaden Understanding of Support Vec tor Machines (Chi-Square Feature Selection with Pseudo-Labelling in Natural Lang uage Processing)
Study Results from Universitas Ahmad Dahlan Broaden Understanding of Support Vec tor Machines (Chi-Square Feature Selection with Pseudo-Labelling in Natural Lang uage Processing)
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 DailyNews Daily News – Investigators discuss new findings in . According to news reporting out of the UniversitasAhmad Dahlan by NewsRx edit ors, research stated, “This study aims to evaluate the effectiveness of theChi- Square feature selection method in improving the classification accuracy of line ar Support VectorMachine, K-Nearest Neighbors and Random Forest in natural lang uage processing when combined withclassification algorithms as well as introduc ing Pseudo-Labelling techniques to improve semi-supervisedclassification perfor mance.”
Universitas Ahmad DahlanEmerging Techn ologiesK-nearest NeighborMachine LearningNatural Language ProcessingSupp ort Vector MachinesVector Machines
NETL
NSTL
