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Old 07-17-2012, 04:38 PM
data_user data_user is offline
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Default Dependent Data

The independence of data seems to be curtail for both theoretical analysis and practical efficiency. What if the sample (x1,y1)...(xN,yN) consists of correlated points? For example, x1....xN is a realization of a Markov chain. Can we still learn from these data? Do we need to change the standard learning algorithms to account for the dependence? Is it possible to introduce a notion of "effective" number of data points N'<N and then work with the sample if it were independent of size N'?
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