CSVM

AcronymDefinition
CSVMCentro Servizi Volontariato Mantovano (Italian: Volunteer Service Center Mantovano)
References in periodicals archive ?
We currently have some setting of the [[alpha].sub.i]s that satisfy the constraint conditions (14) and suppose we have decided to hold [{[[alpha].sub.i]}.sup.n.sub.i=1]\[[alpha].sub.i], [[alpha].sub.j] fixed and reoptimize the dual problem of CSVM (10) with respect to [[alpha].sub.i], [[alpha].sub.j] (subject to the constraints).
From the above algorithm derivation of view, solving of CSVM seems to have been successful, but the computational complexity of the solving process is also larger.
Evaluation of the performance of the three algorithms using CSVM was 10-fold cross-validation tested average misclassification cost, the training time, and the recognition rate of the positive class (i.e., the recognition rate of the minority class).
Training time in seconds is used to evaluate the convergence speeding of three algorithms using CSVM on the same computer.
Cost-sensitive parameters of three algorithms using CSVM are specified as the following Table 2.
The average misclassification cost comparison of three algorithms using CSVM is shown in Figure 1 for each of the datasets.
The recognition rate of positive class comparison of three algorithms using CSVM is shown in Figure 2 for each of the datasets.
The training time comparison of three algorithms using CSVM is shown in Figure 3 for each of the datasets.
We compare the proposed method with classical support vector machines (CSVMs), SVM with multiple parameters based on the radius-margin bound (RW), and SVMs with multiple parameters based on the span bound (Span).
Data set L2SOCP L1L2SOCP CSVMs ASOCP SVM (Span) SVM (RW) Australian 14.21 14.01 15.05 15.01 14.09 14.36 Breast 3.06 2.91 2.91 2.92 2.98 3.02 German 23.10 23.90 23.55 23.57 23.70 23.76 Heart 17.04 16.76 17.41 17.48 17.31 17.26 Ionosphere 11.11 11.39 11.85 11.84 11.29 11.56 Diabetes 22.40 22.79 22.79 22.69 22.68 22.54 Liver 30.67 30.15 31.81 31.79 30.55 31.28 Sonar 22.18 23.01 23.48 23.55 22.35 23.58 Data set IM Australian 14.28 Breast 2.93 German 23.32 Heart 17.38 Ionosphere 11.78 Diabetes 22.80 Liver 31.13 Sonar 23.27 TABLE 3: LOOCV accuracy (%) of the colon data set.