..............................................*...........................................*...............*.....................................*
optimization finished, #iter = 45350
nu = 0.369071
obj = -353.825771, rho = 4.028455
nSV = 131, nBSV = 110
Total nSV = 131
............*
optimization finished, #iter = 3876
nu = 0.376586
obj = -360.192230, rho = -0.999613
nSV = 171, nBSV = 92
Total nSV = 171
.........*.*
optimization finished, #iter = 3167
nu = 0.370124
obj = -354.101978, rho = -0.998724
nSV = 188, nBSV = 83
Total nSV = 188
...........*
optimization finished, #iter = 3737
nu = 0.363636
obj = -348.003879, rho = -0.999886
nSV = 175, nBSV = 92
Total nSV = 175
............................................................................................................*.......................................................................................*
optimization finished, #iter = 62106
nu = 0.353398
obj = -336.812584, rho = 5.759912
nSV = 124, nBSV = 102
Total nSV = 124
..........*
optimization finished, #iter = 4071
nu = 0.371102
obj = -444.103797, rho = -0.999718
nSV = 213, nBSV = 111
Total nSV = 213
Accuracy = 80.9524% (323/399) (classification)
Mean squared error = 0.190476 (regression)
Squared correlation coefficient = -1.#IND (regression)
Accuracy = 80.3704% (217/270) (classification)
Mean squared error = 0.196296 (regression)
Squared correlation coefficient = -1.#IND (regression)