I am training an SVM and I have 40k Negative Samples and 17k Positive samples. What I did is that I have divided my samples into training and testing subsets. In order to train the SVM I have used some of the training (not all) and I was randomly picking samples and apply the SVM into all testing data. I have found that if I use a specific amount of training data (not all) I get a very good performance over the testing data (0.99 sensitivity and 0.99 specificity).
How do I use k fold cross-validation now? I am confused. As I understood it, in k fold all the available data are used and they are divided in 5 subsets etc. Which will be the final SVM that I will use in 'real time'? The one of that I have found with my own good results?
I am using MATLAB (