saygin <[email protected]> wrote:
> 
> Thanks for reply,
>
> I have one more question, how it calculate accuracy with 10-fold cross 
> correlation?
>

My interpretation of the procedure:

n-fold coress-validation consists of taking the learning samples,
splitting them in n sets and perform the training with n-1 sets and
validation with the remaining one.

This is done n times (one for every set) and the accuracy is an average
of the n estimated accuracies.

The real code is in Utilities/otbsvm/svm.cpp starting at line 2362.

Jordi

> On Tuesday, 28 January 2014 13:32:32 UTC+1, Jordi Inglada wrote:
>
>     saygin <[email protected]> wrote:
>     >
>     > Hello,
>     >
>     > I am trying to train my .tif image with approximately 1500 pixels. I 
> have 4 classes and one of them has the lowest training with 115 pixels. If I 
> chose training and validation sample ratio 0.5, and  using SVM OpenCV RBF with
>     parameter
>     > optimization I get 232 training and 184 validation at log file. I am 
> not very familiar with OTB and want to ask does not it use approximately half 
> of all training pixels?
>     >
>    
>     No. It will set the number of samples per class to the number of samples 
> of the class which has the fewer samples. In your case 115. If you have 4 
> classes, you get:
>    
>     115 * 0.5 * 4 = 230
>    
>     Then, why the number of training samples is not equal to the number of 
> validation? I think it is because first the samples are selected, and then 
> they are randomly split between training and validation, and this may 
> introduce some
>     variation accros classes.
>    
>     The current choices for the sampling procedure need improvement and some 
> thinking about this is ongoing 
> (http://wiki.orfeo-toolbox.org/index.php/Refactoring_of_the_classification_chain).
>  Do not hesitate to give you 2 cents about
>     it.
>    
>     Jordi
>
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