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Old 09-03-2012, 07:48 AM
itooam itooam is offline
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Join Date: Jul 2012
Posts: 100
Default Re: Should SVMs ALWAYS converge to the same solution given the same data?

I have looked in more detail, and it is the larger values of C where the discrepancies occur. I.e., when C >= 100... this may make more sense as from what I remember from the lectures, the higher the value of C, the more you tend towards a "hard" margin, which means the classifier will be more sensitive to noise in the data.

Keith that is unusual (maybe you were using low values of C in your tests)?

I did a quick google, this explains it well:

http://stackoverflow.com/questions/4...r-soft-margins

I will put it down to "sensitivity to noise" cause by high C (harder margins), unless otherwise corrected?

For clarity here are my results over 3 runs of Q7 when the power (Q) is set to 5. Each run has a shuffled training and test set:

C_________ NoOfSVs____ Eout_______ Ein
1.0000e-002 2.3000e+001 2.1226e-002 3.8437e-003
1.0000e+000 2.1000e+001 2.1226e-002 3.2031e-003
1.0000e+002 1.3000e+001 2.1226e-002 3.8437e-003
1.0000e+004 1.1000e+001 3.0660e-002 1.7937e-002
1.0000e+006 9.0000e+000 6.3679e-002 5.1249e-002

C_________ NoOfSVs____ Eout_______ Ein
1.0000e-002 2.3000e+001 2.1226e-002 3.8437e-003
1.0000e+000 2.1000e+001 2.1226e-002 3.2031e-003
1.0000e+002 1.3000e+001 2.3585e-002 4.4843e-003
1.0000e+004 9.0000e+000 3.3491e-001 3.3312e-001
1.0000e+006 9.0000e+000 3.3726e-001 3.4465e-001

C_________ NoOfSVs____ Eout_______ Ein
1.0000e-002 2.3000e+001 2.1226e-002 3.8437e-003
1.0000e+000 2.1000e+001 2.1226e-002 3.2031e-003
1.0000e+002 1.3000e+001 2.3585e-002 3.2031e-003
1.0000e+004 9.0000e+000 3.5377e-001 3.4145e-001
1.0000e+006 1.1000e+001 3.5142e-001 3.5106e-001
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