model.dat

这是一个采用c++编写的用于机器学习文本分类的SVM算法的实现代码。

SVM-light Version V6.01
0 # kernel type
3 # kernel parameter -d 
1 # kernel parameter -g 
1 # kernel parameter -s 
1 # kernel parameter -r 
empty# kernel parameter -u 
9947 # highest feature index 
2000 # number of training documents 
879 # number of support vectors plus 1 
-0.13217617 # threshold b, each following line is a SV (starting with alpha*y)
-1.0000000047087669 6:0.013155501 9:0.10063701 27:0.038305663 41:0.12115256 63:0.056871183 142:0.020468477 206:0.12547429 286:0.073713586 406:0.12335037 578:0.40131235 720:0.13097784 960:0.30321017 1607:0.17021149 2205:0.5118736 3177:0.54580438 4507:0.27290219 #
-0.61395616277495313 6:0.019937159 27:0.019350741 31:0.025329925 37:0.031444062 42:0.11928168 83:0.03443896 127:0.066094264 142:0.0086166598 162:0.035993244 190:0.056980081 202:0.16503957 286:0.074475288 323:0.056850906 386:0.052928429 408:0.039132856 411:0.049789339 480:0.048880257 500:0.068775021 506:0.037179198 555:0.076585822 594:0.063632675 663:0.062197074 673:0.067195281 782:0.075720288 834:0.066969693 923:0.44677126 1146:0.076086208 1191:0.5542227 1225:0.059279677 1302:0.094811738 1305:0.060443446 1379:0.070145406 1544:0.087077379 1936:0.089480147 2451:0.31556693 2796:0.1145037 2833:0.20080972 6242:0.1545693 6574:0.28386003 7639:0.29435158 #
-0.54795085194856674 6:0.030920738 11:0.015438613 15:0.011770805 18:0.18390873 27:0.010003753 31:0.039284434 33:0.019011701 41:0.015819857 63:0.0074261245 75:0.0069629033 83:0.0089019546 98:0.02228589 111:0.021485684 118:0.089988098 142:0.0071272869 172:0.026942596 200:0.033190284 207:0.040629525 226:0.079918697 256:0.023380557 275:0.04620656 285:0.073276632 286:0.07700298 295:0.058341954 308:0.05076988 351:0.024381585 355:0.041354887 367:0.029323906 382:0.030176234 398:0.13361384 408:0.020230513 423:0.026030142 426:0.030928768 433:0.055566143 439:0.037029117 478:0.026942596 480:0.050539255 483:0.033357725 485:0.023775641 502:0.023714704 504:0.036495842 508:0.026656149 513:0.025709525 515:0.024821982 535:0.032579821 550:0.055982187 575:0.035858039 578:0.05240256 585:0.042570043 656:0.033914451 663:0.12861609 671:0.02098156 723:0.03526067 726:0.031661574 761:0.069871157 764:0.031775154 800:0.030297557 806:0.036880661 824:0.076848499 836:0.038959667 922:0.14651586 923:0.51967651 953:0.075083137 963:0.029149331 968:0.047689591 977:0.09447404 1025:0.12563358 1047:0.23230764 1106:0.044023186 1109:0.04753631 1191:0.28651652 1232:0.038898651 1273:0.046126708 1303:0.052660841 1322:0.038599364 1330:0.045372166 1388:0.177807 1410:0.14215642 1503:0.093055815 1538:0.043714084 1664:0.040776823 1781:0.13083978 1782:0.042304482 1956:0.051207699 1963:0.049194388 2067:0.042843301 2205:0.13367885 2453:0.055372752 2456:0.048495788 2529:0.05240256 2674:0.050769169 2837:0.051667634 3395:0.11640354 3448:0.055029925 3887:0.059733797 4453:0.059733797 4511:0.071270041 5176:0.058685299 5549:0.063017204 6197:0.13367885 6239:0.19718733 7292:0.34576732 7579:0.15981539 7699:0.076085471 #
-0.097081314013337278 2:0.038812101 6:0.028522387 29:0.03452925 31:0.036237355 41:0.17511357 42:0.11376406 84:0.055798851 105:0.10099518 134:0.068092026 142:0.0024654234 199:0.044441462 214:0.081454784 286:0.21309084 390:0.086847708 441:0.067041896 452:0.13420272 490:0.098390542 508:0.073765732 599:0.089145139 671:0.058062404 981:0.11284196 998:0.10523477 1045:0.049389366 1056:0.093360387 1058:0.10665374 1191:0.47572741 1245:0.09297391 1289:0.12875699 1391:0.12014205 1612:0.13877322 2285:0.12838162 2574:0.17665775 2634:0.17914408 2837:0.14298017 5555:0.20304713 6319:0.59167808 #
0.046012271801589788 6:0.027157096 15:0.031014221 29:0.032876424 31:0.034502767 83:0.023455251 97:0.16945615 102:0.17251198 134:0.064832643 142:0.0023474102 158:0.064973764 174:0.066356033 181:0.076408297 196:0.25253487 220:0.07261958 277:0.081978798 278:0.078149997 375:0.1925191 413:0.078270569 427:0.096160807 467:0.11362685 502:0.062484518 505:0.065115698 587:0.11465125 697:0.09704572 764:0.083722532 778:0.086507306 796:0.079573236 847:0.096536204 863:0.095668949 876:0.087363876 987:0.073903739 1046:0.10170326 1154:0.12295633 1359:0.1004914 1676:0.12295633 1775:0.7341795 2171:0.1252507 4140:0.16405231 6142:0.15335251 6537:0.17942803 #
0.55323570233436725 6:0.019840326 15:0.033987373 29:0.036028098 31:0.037810348 41:0.045678727 63:0.021442413 74:0.08132381 75:0.020104894 81:0.060399663 142:0.010289771 172:0.077794857 174:0.072717197 179:0.054076742 180:0.076445669 186:0.11258671 187:0.092501171 205:0.15699077 229:0.051950566 255:0.09233211 308:0.073297232 318:0.11974088 408:0.058414184 409:0.050662618 465:0.084354579 480:0.072964273 519:0.1186113 664:0.11214209 679:0.37438783 720:0.098766401 768:0.1239752 922:0.14101808 977:0.13639358 1018:0.10764876 1305:0.18044963 1581:0.14152686 1677:0.15612461 1817:0.14101808 2162:0.17092134 2314:0.16424933 2358:0.50834906 2419:0.15058282 3266:0.33889937 3374:0.1667255 8311:0.21969146 #
-0.58552914399292433 6:0.029228659 17:0.053090084 27:0.085106842 51:0.066170633 83:0.050488871 142:0.022738252 190:0.16707008 228:0.073685274 286:0.16377555 295:0.16544789 411:0.072993129 413:0.084241107 424:0.40138948 496:0.081699118 507:0.10418942 510:0.088081591 515:0.070390932 517:0.10863864 521:0.096074179 686:0.57609224 710:0.33683348 1105:0.1222944 1127:0.11227783 1645:0.11136536 2047:0.13395619 2784:0.16131414 3264:0.16642156 3566:0.18954529 4309:0.17274088 7448:0.21576551 #
0.52817794377041916 6:0.010149107 15:0.034771759 26:0.021964068 29:0.01842979 31:0.019341482 63:0.010968639 75:0.010284444 81:0.030896803 83:0.013148489 97:0.031664442 134:0.036343727 142:0.0026318112 178:0.040953908 229:0.026574759 255:0.047231507 276:0.15768459 322:0.043978736 357:0.046975236 365:0.053767055 382:0.089142643 431:0.05658412 432:0.047669392 478:0.11938539 640:0.047361434 679:0.063838035 687:0.057099868 774:0.11385164 823:0.057818666 880:0.05969109 1097:0.081281066 1126:0.054116037 1216:0.054329701 1232:0.057454631 1260:0.067749217 1330:0.067016229 1436:0.068130717 1599:0.061490189 1831:0.1374466 2264:0.068926565 2522:0.089072965 2643:0.073776267 2740:0.073489174 2792:0.077781856 2852:0.087432988 2965:0.077400357 3785:0.086680181 4124:0.09561713 4311:0.17814593 4521:0.097084127 5113:0.50291675 6488:0.09561713 7377:0.10272969 7846:0.23605275 8114:0.10526827 9234:0.51071006 9235:0.38303256 #