I'm training a classification SVM for a binary classification model with 20 features (LIBSVM). These are some reports of my model (with different combination of features). Why all nBSVs are zero? Otherwise, based on *.* and .* outputs, the SVM trains real fast. Is this a problem?
.*
optimization finished, #iter = 1194
nu = 0.007242
obj = -296.242686, rho = -0.026875
nSV = 617, nBSV = 0
Total nSV = 617
*.*
optimization finished, #iter = 924
nu = 0.007236
obj = -296.473015, rho = 0.002460
nSV = 618, nBSV = 0
Total nSV = 618
.*
optimization finished, #iter = 1204
nu = 0.007288
obj = -298.117823, rho = -0.021878
nSV = 618, nBSV = 0
Total nSV = 618
.*
optimization finished, #iter = 1169
nu = 0.007248
obj = -296.465051, rho = -0.012346
nSV = 618, nBSV = 0
Total nSV = 618
.*
optimization finished, #iter = 1449
nu = 0.007177
obj = -367.201873, rho = -0.015914
nSV = 772, nBSV = 0
Total nSV = 772
.*.*
optimization finished, #iter = 1355
nu = 0.008642
obj = -300.306592, rho = -0.028429
nSV = 617, nBSV = 0
Total nSV = 617
.*
optimization finished, #iter = 1196
nu = 0.008608
obj = -298.631753, rho = -0.017603
nSV = 616, nBSV = 0
Total nSV = 616
*.*
optimization finished, #iter = 774
nu = 0.008684
obj = -301.772061, rho = 0.000564
nSV = 618, nBSV = 0
Total nSV = 618
.*.*
optimization finished, #iter = 1313
nu = 0.008646
obj = -299.953007, rho = -0.026729
nSV = 617, nBSV = 0
Total nSV = 617
.*
optimization finished, #iter = 1107
nu = 0.008649
obj = -300.051120, rho = -0.007149
nSV = 617, nBSV = 0
Total nSV = 617
.*
optimization finished, #iter = 1482
nu = 0.008603
obj = -373.305016, rho = -0.013622
nSV = 771, nBSV = 0
Total nSV = 771
From LIBSVM documentation:
obj is the optimal objective value of the dual SVM problem. rho is the bias term in the decision function sgn(w^Tx - rho). nSV and nBSV are number of support vectors and bounded support vectors (i.e., alpha_i = C). nu-svm is a somewhat equivalent form of C-SVM where C is replaced by nu. nu simply shows the corresponding parameter.
