On 04/17/2012 10:26 AM, Emanuele Olivetti wrote: [...] > More precisely, I see three options: > a) Each set of instances is drawn from its own (different) distribution. > b) The two set of instances are two distinct draws from the same > distribution. Then > they are represented in different feature spaces. > c) There is just one common set of instances. The two datasets and are just > two > representations of it in two feature different spaces (pictures and drawings, > respectively). > > My opinion is that option 'a' leads to an ill posed problem, option 'b' > leads to a difficult problem. Option 'c' inot that easy to address anyway > but I have a ready solution for it :-). >
After more thoughts I guess I have no solution for 'c' either. I am working on a related problem, i.e. "are the two classifiers statistically dependent or not?". Your problem seems is related to "are the two classifier behaving/predicting in the same way or not?", which - I guess - requires even more details to be addressed. Best, Emanuele ------------------------------------------------------------------------------ Better than sec? Nothing is better than sec when it comes to monitoring Big Data applications. Try Boundary one-second resolution app monitoring today. Free. http://p.sf.net/sfu/Boundary-dev2dev _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
