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joyhuang9473/coursera-stanford-machine-learning-class-week7-try-different-svm-parameters.m

Last active November 23, 2015 14:51
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Automatically choose optimal C and sigma based on a cross-validation set.
Raw
coursera-stanford-machine-learning-class-week7-try-different-svm-parameters.m
% Octave console output
% C list: [0.01 0.03 0.1 0.3 1 3 10 30]
% sigma list: [0.01 0.03 0.1 0.3 1 3 10 30]
Training ......... Done!
C: 0.010000
sigma: 0.010000
error = 0.56500
====================
Training ........................................ Done!
C: 0.010000
sigma: 0.030000
error = 0.060000
====================
Training ......................................................................
.. Done!
C: 0.010000
sigma: 0.100000
error = 0.045000
====================
(...)
Training ......................................................................
.............................. Done!
C: 30.000000
sigma: 3.000000
error = 0.065000
====================
Training ....................................................... Done!
C: 30.000000
sigma: 10.000000
error = 0.10000
====================
Training .................................................. Done!
C: 30.000000
sigma: 30.000000
error = 0.18000
====================
optimal C = 1.000000 and sigma = 0.100000
Program paused. Press enter to continue.
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