We study the behavior of error bounds for multiclass classification under suitable margin conditions. For a wide variety of methods we prove that the classification error under a hard-margin condition decreases exponentially fast without any bias-variance trade-off. Different convergence rates can be obtained in correspondence of different margin assumptions. With a self-contained and instructive analysis we are able to generalize known results from the binary to the multiclass setting.

Multiclass Learning with Margin: Exponential Rates with No Bias-Variance Trade-Off

Vigogna S.;Meanti G.;De Vito E.;Rosasco L.
2022-01-01

Abstract

We study the behavior of error bounds for multiclass classification under suitable margin conditions. For a wide variety of methods we prove that the classification error under a hard-margin condition decreases exponentially fast without any bias-variance trade-off. Different convergence rates can be obtained in correspondence of different margin assumptions. With a self-contained and instructive analysis we are able to generalize known results from the binary to the multiclass setting.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1135076
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