[pymvpa] Does it make sense to compare SVM weights between two different SVM classifiers?

[Meng Liang]

Dear MVPA experts,

In my study, I used the fMRI signals from a given ROI to predict the stimulus type for two different classification tasks: (1) type A vs. type B, and (2) type C vs. type D (the two classification tasks were performed on the same ROI but during different trials: the fMRI data used for task 'A vs. B' were taken from trials A and trials B, and the data used for task 'C vs. D' were taken from trials C and trials D). It was expected that this ROI should provide a higher classification accuracy in the task of 'A vs. B' than in the task of 'C vs. D'. The results indeed confirmed this. I just wonder whether the higher classification accuracy in the task of 'A vs. B' (presumably the higher capability of the classifier in task 'A vs. B') relative to the task 'C vs. D' could be reflected in the sensitivity maps (i.e., SVM weights) in some way? For example, would the SVM of task 'A vs. B' have higher SVM weights or a larger margin compared to the SVM of task 'C vs. D'? In other words, can I directly compare the sensitivity maps obtained from the two different classification tasks?

I'm not sure if I asked my question clearly. Please let me know if there is anything unclear.

Many thanks!
Meng


 		 	   		  
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