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I have training instances that come from different sources so building one model doesn't work well. Is there a known method to use in such cases?

Example explains best. Let's say I want to classify cancer/non-cancer given training data that was constructed based on different populations. Training instances from one population might have a completely different distribution of positive/negative examples than in other populations. Now, I can build a separate model for each population, but the problem is that for testing I don't know from which population the test instance is coming from.

*all training/testing instances have the exact same feature set regardless of the population they came from.

Thanks a lot!

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Suggest reposting or migrating to stats.SE.com. – zyx Sep 2 '11 at 21:50
Thanks, I didn't know about stats.stats.SE.com – Raviv Sep 2 '11 at 21:52
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I voted to migrate to CV. – J. M. Sep 3 '11 at 1:44
This question was crossposted to stats.SE as well as SO. – t.b. Sep 4 '11 at 21:22
@J.M. I'm not seeing an off-topic penalty here :) But maybe it's "denormalized"... – t.b. Sep 4 '11 at 21:48
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closed as off topic by J. M., joriki, Asaf Karagila, t.b., mixedmath♦ Sep 4 '11 at 21:34

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