首页|Researchers from Guizhou University of Finance and Economics Discuss Findings in Support Vector Machines (Some Notes On the Basic Concepts of Support Vector Mac hines)
Researchers from Guizhou University of Finance and Economics Discuss Findings in Support Vector Machines (Some Notes On the Basic Concepts of Support Vector Mac hines)
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Data detailed on Support Vector Machin es have been presented. According to news reporting from Guizhou, People's Repub lic of China, by NewsRx journalists, research stated, "Support vector machines ( SVMs) are classic binary classification algorithms and have been shown to be a r obust and well-behaved technique for classification in many real-world problems. However, there are ambiguities in the basic concepts of SVMs although these amb iguities do not affect the effectiveness of SVMs." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Basic search Project of Guizhou Province, China. The news correspondents obtained a quote from the research from the Guizhou Univ ersity of Finance and Economics, "Corinna Cortes and Vladimir Vapnik, who presen ted SVMs in 1995, pointed out that an SVM predicts through a hyperplane with a m aximal margin. However existing literatures have two different definitions of th e margin. On the other hand, Corinna Cortes and Vladimir Vapnik converted an SVM into an optimization problem that is much easier to solve. Nevertheless, existi ng papers do not explain how the optimization problem derives from an SVM well. These ambiguities may cause certain troubles in understanding the basic concepts of SVMs. For this purpose, this paper defines a separating hyperplane of a trai ning data set and, hence, an optimal separating hyperplane of the set. The two d efinitions are reasonable since this paper proves that wT0x+b0 T 0 x + b 0 = 0 i s an optimal separating hyperplane of a training data set when w0 0 and b 0 cons titute a solution to the above optimization problem. Some notes on the above mar gin and optimization problem are given based on the two definitions."
GuizhouPeople's Republic of ChinaAsiaEmerging TechnologiesMachine LearningSupport Vector MachinesVector Mach inesGuizhou University of Finance and Economics
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