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Abstract:
We investigate the generalization performance of some learning
problems in Hilbert functional Spaces. We introduce a notion of
convergence of the estimated functional predictor to the best
underlying predictor, and obtain an estimate on the rate of the
convergence. This estimate allows us to derive generalization
bounds on some learning formulations.
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