首页|Findings from Changsha University of Science and Technology Provides New Data ab out Support Vector Machines (Sparse and Robust Support Vector Machine With Capped Squared Loss for Largescale Pattern Classification)
Findings from Changsha University of Science and Technology Provides New Data ab out Support Vector Machines (Sparse and Robust Support Vector Machine With Capped Squared Loss for Largescale Pattern Classification)
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 publish new report on Su pport Vector Machines. According to newsreporting from Changsha, People’s Repub lic of China, by NewsRx journalists, research stated, “Supportvector machine (S VM), being considered one of the most efficient tools for classification, has re ceivedwidespread attention in various fields. However, its performance is hinde red when dealing with large-scalepattern classification tasks due to high memor y requirements and running very slow.”
ChangshaPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningSupport Vector MachinesVector Mac hinesChangsha University of Science and Technology
NETL
NSTL
