On Thu, 31 Jan 2013, Michael Hanke wrote: > As the subject "clearly" says, I would like to train a classifier using > classical methods for example using all voxels and then try to predict > using only a portion of ROI like a searchlight. > I've tried to do this using the classifier as data measure in > searchlight class, but obviously the features of the classifier are more > than those used in the searchlight. > How can I do?
> This is less of a technical question, but more of a conceptual one. You > can't train an algorithm on one set of features and then run it on a > different one with a different number of features. > you need to have equally structured input in both training and testing > stage. This could be done (think e.g. PCA projection), but whether it > makes sense in you context is impossible to tell at this point. indeed! But I guess it could be stretched to become a "technically legit" one in the case of kernel-based classifiers, where optimization and decision is done based on values within the kernel... theoretically it should be possible to get the solution for one kernel (estimated on full data) and then apply to another (estimated on subset of the features)... not sure how legit it would be, but at least possible technically -- I guess could become an improved "sensitivity" measure to complement existing ones ;-) -- Yaroslav O. Halchenko Postdoctoral Fellow, Department of Psychological and Brain Sciences Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755 Phone: +1 (603) 646-9834 Fax: +1 (603) 646-1419 WWW: http://www.linkedin.com/in/yarik _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa
