Hi experts, I am talking about basic pattern classification (e.g. no feature selection etc). SVM algorithm (with built-in regularization).
1. A small number of data points with large dimension (ROI size) can cause overfitting, which is high prediction on training set and bad test set. Now, suppose, I have a beyond chance classification on test set, which was validated using within subject permutation test and across subjects t-test vs. chance. Can my results be still unreliable? If so, how can I test it? 2. Practically, is 10 independent data points (averaged block value or beta values) with the ROI of 100 voxels is safe enough? 3. Do you know about any imaging papers which tested / discussed this issue? Thanks for ideas, Vadim
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