Hi all. Is it possible to "convince" matlab fitcsvm function to use a well-defined (not random) subset of the sample vectors for training (leaving the others for testing)? Not simply a random percentage, as set by the "'Holdout', value" pair, but a list of indices (decided by me) to exactly choose the desired samples from the whole dataset.
If I could have a percentage equal to 0 in Holdout, it would do, because the machine would be trained on all the input vectors, then I'd use "predict" on the test subset.
This is absolutely necessary for my code, because I must be able to use the same sample subsets for training etc of different classifiers.
To be clearer, when using a neural network (by patternnet, in the Neural Network Toolbox), I can decide which sample vectors to use for training, validation, and test, by net.divideFcn = 'divideind', then setting manually the indices to be used for training etc.
Thanks.
Best regards.
Giorgio
Replying to myself... I was not understanding but perhaps now it is clear.
By training fitcsvm with a simple fitcsvm(x,y) I can train the machine with the whole set of data (everything is used as the training set). The trained machine can then be applied to a new (test) data set by the predict function. This is exactly what I need.
Only if I set an option such as 'CrossVal', 'CVPartition', etc, I get a ClassificationPartitionedModel, with a number of machines trained accordingly. Otherwise, I get a ClassificationSVM classifier.
It was simple...
Bye
Giorgio
I was about to propose you to split the data into two sets. your reply shows the answer,
best regards
Hi Nelly, thank you very much!
Yes, I found the solution after a hard (and stupid) struggle :-D... it was easy and direct....
My svm's are now happily working, with AUC above 0.96 after all :-).
It is a problem of Brain-Computer Interfacing....
Thanks again
Best regards
Giorgio
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