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NPA algorithm and its amendments

sample size n and m. Table 1 Table 1lCheckers0.5Z465AduIt a 110.01Z31605AduIt ~ 410.01Z34781 penalties for different coefficients;, a number of experiments,[link widoczny dla zalogowanych], each are equipped with s-O. 001,[link widoczny dla zalogowanych], initial value of each type of training focused on the first occurrence in the training sample. Experiment will examine the training time T (CPU Time: sec), there are margin support vector machine and support vector number # SV. To the same;,[link widoczny dla zalogowanych], modified by NPA and the NPA is almost the same margin and # SV. Therefore, these findings are not listed here. There were significant differences in training time. Concrete results in Table 2 to 4. Table 2CheckersI10I50I1001500l103l104Il10 ・ I10'100NPAI2I3I5I10I13l2857I83l6685 amended NPAl2I3l4I8l12l2753I66I7566 Table 3Adult a 1C0.030.102030.6I. 0203.05101050IO050DINPA7268727I798793102II6I432 = I83 plus 509,634 amendment 636I65647079849Il03I242002674I55o3NPA table 4Adult. 4l0.030. I10, 2l0.310.6I1.0l2.0l3.0l5.0l10I50I100l500I10 = INPAl586573I636I688I808I86Il949 [994I1181ll5I9f3I58l3957I8652I12449 amended NPAI527526I583l629l745l79Il8731899I1055l1323126691330317106I10293 general,[link widoczny dla zalogowanych], when; increase (margin decreases), the training time is gradually increasing. The number of support vectors decreases. In; under the same conditions. Our algorithm compared with the NPA to obtain almost the same margin and the number of support vectors, the training time is reduced. The average reduction of 10% or more. ; Increases, a greater reduction. Each experiment the same characteristics emerged. Shows the performance of the new algorithm is stable.
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