svm.r

> tuning.svm("normalized data",ntrn,cvec,10,0.7)
[1] "normalized data"
[1] "#########linear SVM#################"
[1] "parameter= 1 the best cost= 3.2 the error = 13.0911820319358"
[1] "########polynomial kernel SVM##########"
[1] "parameter= 2 the best cost= 1 the error = 36.0239737549915"
[1] "parameter= 3 the best cost= 1 the error = 15.5345486240454"
[1] "parameter= 4 the best cost= 0.32 the error = 33.5509708662857"
[1] "parameter= 5 the best cost= 0.32 the error = 41.9638713171909"
[1] "parameter= 6 the best cost= 0.032 the error = 45.8911349089365"
[1] "parameter= 7 the best cost= 0.01 the error = 52.4204054549299"
[1] "########RBF kernel SVM###############"
[1] "parameter= 0.05 the best cost= 100 the error = 73.8399571148679"
[1] "parameter= 0.1 the best cost= 100 the error = 76.136578442901"
[1] "parameter= 0.2 the best cost= 3.2 the error = 67.1695151573611"
[1] "parameter= 0.3 the best cost= 100 the error = 58.8905466491342"
[1] "parameter= 0.4 the best cost= 100 the error = 49.190563732546"
[1] "parameter= 0.5 the best cost= 100 the error = 39.3667236728242"
[1] "parameter= 0.8 the best cost= 3.2 the error = 32.1457803322543"
[1] "parameter= 5 the best cost= 32 the error = 6.90665923311557"