fitcsvm: how can I decide training (and test) data set composition?

Hi all. Is it possible to "convince" fitcsvm 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

[Giorgio De Nunzio]

Replying to myself... I think 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