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Old 09-24-2012, 02:51 PM
Andrs Andrs is offline
Join Date: Jul 2012
Posts: 47
Default SVM and C-parameter selection

C parameter defines the penalty for violating the SVM margins. The recommendation is to use the CV to find the best C value. What are some typical criteria used to identify a suitable C value??
Here are some statements but I am not sure if they could be used to select a suitable C value???

If we select a very large C value and there is noisy data (+ non linearly separable data), we may select a too narrow margin (hard margins) and we may overfitt if we are using a high dimensional kernel (try to fit noisy). Here we should get a large number of margin support vectors that indicate poor generalization and large E_out.

If we select a too small C, we should get many non-margin support vectors implying a total large number of support vectors(large E_out). Are we underfitting with too small C value???
We should have something in between!
Should we try to minimize the total number of support vectors as the main criteria for selecting C in order to reduce E_out. Or are there other aspects to take into consideration....???
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c-value, overfitting, svm

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