DDC
| 006 |
Tác giả CN
| Nguyen, The Cuong |
Tác giả TT
| |
Nhan đề
| Weighted Structural Support Vector Machine / Nguyen The Cuong, Huynh The Phung |
Tóm tắt
| 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 |
Từ khóa tự do
| \Veighted structural - support vector machine |
Từ khóa tự do
| Structural twin supp-art vector
machine |
Từ khóa tự do
| Support vector machine |
Từ khóa tự do
| Twin support vector machine |
Tác giả(bs) CN
| Huynh, The Phung |
Nguồn trích
| Tạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics 2021tr. 45-58
Số: 01 |
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000
| 00000nab#a2200000ui#4500 |
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001 | 52400 |
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002 | 9 |
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004 | 2CEC88A4-B560-4FD4-935C-B74035D40B7C |
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005 | 202409181453 |
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008 | 081223s VN| vie |
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009 | 1 0 |
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039 | |y20240918145735|ztainguyendientu |
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040 | |aACTVN |
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041 | |avie |
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044 | |avm |
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082 | |a006 |
---|
100 | 10|aNguyen, The Cuong |
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110 | |bVietnam Academy Of Science And Technology |
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245 | |aWeighted Structural Support Vector Machine / |cNguyen The Cuong, Huynh The Phung |
---|
520 | |a 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 |
---|
653 | |a\Veighted structural - support vector machine |
---|
653 | |aStructural twin supp-art vector
machine |
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653 | |aSupport vector machine |
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653 | |aTwin support vector machine |
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700 | |aHuynh, The Phung |
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773 | 0 |tTạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics |d2021|gtr. 45-58|x1813-9663|i01 |
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890 | |a0|b0|c1|d0 |
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