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Weighted Structural Support Vector Machine / Nguyen The Cuong, Huynh The Phung // Tạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics . - 2021. - tr. 45-58. - ISSN: 1813-9663



Ký hiệu phân loại (DDC): 006
In binary classification problems, two classes of data seem to be different from each other. It is expected to be more complicated due to the clusters in each class his!) tend to be different. Traditional algorithms as Support Vector Machine (SVM) or Twin Support Vector Max-hint: (TVVSVM) cannot sufficiently exploit structural information with cluster granularity- data. cause limitation on the capability of simulation of data trends. Structural Twin Supp-2‘. Vet-tor Machine (S-TVVSVM) sufficiently exploits structural information with cluster granularity: 52-: learning a represented hyperplane. Therefore, the capability of S-TWSVM’S data simulation Ls ten-er than that of TWSVM. However, for the datasets where each class consists of clusters of differs: trends. The S-TWSVM‘s data simulation capability seems restricted. Besides, the training time of has not been improved compared to TWSVM. This paper proposes a new VVeightrai Stair-viral -Support Vector Machine (called WS-SVM) for binary classification problems with a.stersstrategy. Experimental results Show that VVS-SVM could describe the tendency of :351i.utionof cluster information. Furthermore, both the theory and experiment show that the- mating time ofthe WS-SVM for classification problem has significantly improved compared
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