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Multiclass learning with margin: exponential rates with no bias-variance trade-off

Stefano Vigogna · Giacomo Meanti · Ernesto De Vito · Lorenzo Rosasco

Hall E #1216

Keywords: [ MISC: Supervised Learning ] [ T: Learning Theory ]


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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