22.txt.model

基于蚁群算法和支持向量机的最优子集提取 蚁群算法采用蚁群系统!

svm_type nu_svc
kernel_type rbf
gamma 0.0526316
nr_class 7
total_sv 2061
rho -0.137332 -0.116518 0.00274251 -0.0765798 -0.0203736 -0.135139 0.0211518 0.140021 0.0613975 0.117286 0.00224138 0.119253 0.0421697 0.0963744 -0.0189153 -0.0793003 -0.0231153 -0.137898 0.0562941 -0.0591649 -0.115075
label 7 6 5 4 3 2 1
nr_sv 300 288 286 300 296 300 291
SV
0.8558109464786542 0.8764587833195116 0.9947715670314 0.9160797768675049 0.971839205916642 0.8579858289727123 1:110 2:189 3:9 4:0 5:0 6:1 7:0.666667 8:1.222222 9:1.186342 10:12.925926 11:10.888889 12:9.222222 13:18.666668 14:-6.111111 15:-11.111111 16:17.222221 17:18.666668 18:0.508139 19:1.910864 
0.6908645385141774 0.7075327738634325 0.8030423102182268 0.7395173372014411 0.7845298628957557 0.6926202407481374 1:86 2:187 3:9 4:0 5:0 6:1.111111 7:0.720083 8:1.444445 9:0.750309 10:13.740741 11:11.666667 12:10.333334 13:19.222221 14:-6.222222 15:-10.222222 16:16.444445 17:19.222221 18:0.463329 19:1.941465 
0.841192194810295 0.8614873303910116 0.9777791243012642 0.9004315278920333 0.9552384881163758 0.8433299264976015 1:225 2:244 3:9 4:0 5:0 6:3.388889 7:2.195113 8:3 9:1.520234 10:12.259259 11:10.333334 12:9.333334 13:17.111111 14:-5.777778 15:-8.777778 16:14.555555 17:17.111111 18:0.480149 19:1.987902 
0.7292158457316844 0.7468093690050061 0.8476208356018377 0.7805694610413495 0.8280807243394949 0.7310690105968889 1:47 2:232 3:9 4:0 5:0 6:1.277778 7:1.254621 8:1 9:0.894427 10:12.703704 11:11 12:9 13:18.111111 14:-5.111111 15:-11.111111 16:16.222221 17:18.111111 18:0.500966 19:1.875362 
0.8587144543971388 0.8794323431086968 0.9981465263529153 0.91918775871448 0.9751363626560866 0.8608967156102577 1:97 2:186 3:9 4:0 5:0 6:1.166667 7:0.691215 8:1.166667 9:1.00554 10:15.592592 11:13.888889 12:11.777778 13:21.111111 14:-5.111111 15:-11.444445 16:16.555555 17:21.111111 18:0.442661 19:1.863654 
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0.8622369966900542 0.8830398724876368 1.002241034509495 0.9229583692342193 0.9791364805779718 0.8644282097816213 1:27 2:248 3:9 4:0.111111 5:0 6:1.611111 7:0.64693 8:3.166667 9:1.722401 10:15.296296 11:14.777778 12:12.888889 13:18.222221 14:-1.555556 15:-7.222222 16:8.777778 17:18.222221 18:0.312227 19:1.783512 
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0.8483370220550789 0.8688045382623969 0.9860840788723267 0.9080795157742748 0.9633519894270949 0.8504929109764822 1:32 2:240 3:9 4:0 5:0 6:1.277778 7:1.540803 8:2.277778 9:1.705112 10:12.148149 11:9.777778 12:9.333334 13:17.333334 14:-7.111111 15:-8.444445 16:15.555555 17:17.333334 18:0.470675 19:2.043865 
0.8303228675624761 0.8503557628709616 0.965144911391303 0.8887967492988733 0.9428955303582024 0.8324329768997172 1:55 2:209 3:9 4:0.111111 5:0 6:1.833333 7:1.295291 8:1.388889 9:0.998146 10:12.518518 11:10.333334 12:10.333334 13:16.888889 14:-6.555555 15:-6.555555 16:13.111111 17:16.888889 18:0.407904 19:2.09922 
0.8499732908569139 0.8704802847215195 0.9879860453661256 0.9098310156412115 0.9652100986036422 0.8521333380476024 1:138 2:203 3:9 4:0 5:0 6:1.666667 7:1.646545 8:2.277778 9:1.878731 10:11.259259 11:8.777778 12:8.111112 13:16.888889 14:-7.444445 15:-9.444445 16:16.888889 17:16.888889 18:0.538945 19:2.019747 
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0.8498533104147128 0.8703574095551917 0.9878465715307608 0.9097025858084636 0.9650738515760082 0.8520130526976825 1:58 2:217 3:9 4:0 5:0 6:1.388889 7:1.06284 8:1.666667 9:1.349897 10:11.925926 11:9.777778 12:9 13:17 14:-6.444445 15:-8.777778 16:15.222222 17:17 18:0.486598 19:2.006642 
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0.8612648051424309 0.8820442252310908 1.00111098527758 0.921917712977503 0.9780324822410392 0.8634535475921038 1:19 2:184 3:9 4:0 5:0 6:0.555556 7:0.344265 8:0.833333 9:0.62361 10:14.222222 11:12.333333 12:11 13:19.333334 14:-5.666667 15:-9.666667 16:15.333333 17:19.333334 18:0.431152 19:1.937891 
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