Deterministic Bounds on Accuracy and Stability of Metric-Space Regression and Classification
A geometric framework that quantifies ambiguity of the input-target relation through the average kernel size, computed with data-dependent, i.e. model-independent, feasible sets derived from worst-case input and target noise is introduced.
N. Gottschling, Jakob Gawlikowski, Michele Caprio
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