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Published on: October 9, 2013
Stochastic separation theorems
1Department of Mathematics, University of Leicester, Leicester, LE1 7RH, UK.
Abstract:
The problem of non-iterative one-shot and non-destructive correction of unavoidable mistakes arises in all Artificial Intelligence applications in the real world. Its solution requires robust separation of samples with errors from samples where the system works properly. We demonstrate that in (moderately) high dimension this separation could be achieved with probability close to one by linear discriminants. Based on fundamental properties of measure concentration, we show that for M
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