首页|Study Data from Changsha University of Science and Technology Update Understanding of Support Vector Machines (Fast Generalized Ramp Loss Support Vector Machine for Pattern Classification)
Study Data from Changsha University of Science and Technology Update Understanding of Support Vector Machines (Fast Generalized Ramp Loss Support Vector Machine for Pattern Classification)
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NETL
NSTL
Elsevier
New research on Support Vector Machines is the subject of a report. According to news reporting originating from Changsha, People’s Republic of China, by NewsRx correspondents, research stated, “Support vector machine (SVM) is widely recognized as an effective classification tool and has demonstrated superior performance in diverse applications. However, for large-scale pattern classification problems, it may require much memory and incur prohibitive computational costs.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Changsha Munici-pal Natural Science Foundation, Hunan Provincial Education Department. Our news editors obtained a quote from the research from the Changsha University of Science and Technology, “Motivated by this, we propose a new SVM model with novel generalized ramp loss (LRSVM). The first-order optimality conditions for the non-convex and non-smooth LR-SVM are developed by the newly developed P-stationary point, based on which, the LR support vectors and working set of LR-SVM are defined, interestingly, which shows that all of the LR support vectors are on the two support hyperplanes under mild conditions. A fast proximal alternating direction method of multipliers with working set (LR-ADMM) is developed to handle LR-SVM and LR-ADMM has been demonstrated to achieve global convergence while maintaining a significantly low computational complexity.”
ChangshaPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningSupport Vector MachinesVector MachinesChangsha University of Science and Technology
NETL
NSTL
Elsevier
