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Old 04-20-2012, 05:39 AM
DASteines DASteines is offline
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Default A Modification to the Learning Diagram

How does the learning problem change if the training samples are drawn from an indexed set of distributions? That is, suppose our training samples, x and y, are drawn from:

p(x,y,\theta) where \theta = {1,2,...,k}

Suppose I am trying to classify groups of pixels in images. I have 10 images that I can draw groups of pixels from. The images are indexed by theta, with k=10. How do we account for the grouping of the training data? What strategies exist to build a "good" (unbiased) training set in cases like this?
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