TRA CỨU
thông tin biểu ghi
  • Bài trích
  • Ký hiệu PL/XG: 006
    Nhan đề: Weighted Structural Support Vector Machine /
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
000 00000nab#a2200000ui#4500
00152400
0029
0042CEC88A4-B560-4FD4-935C-B74035D40B7C
005202409181453
008081223s VN| vie
0091 0
039|y20240918145735|ztainguyendientu
040 |aACTVN
041 |avie
044 |avm
082 |a006
10010|aNguyen, The Cuong
110 |bVietnam Academy Of Science And Technology
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
653 |aSupport vector machine
653 |aTwin support vector machine
700 |aHuynh, The Phung
7730 |tTạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics |d2021|gtr. 45-58|x1813-9663|i01
890|a0|b0|c1|d0
Không tìm thấy biểu ghi nào