ICML 2022spotlight5 citations
Multiclass learning with margin: exponential rates with no bias-variance trade-off
Stefano Vigogna, Giacomo Meanti, Ernesto De Vito, Lorenzo Rosasco
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.
BibTeX
@InProceedings{pmlr-v162-vigogna22a,
title = {Multiclass learning with margin: exponential rates with no bias-variance trade-off},
author = {Vigogna, Stefano and Meanti, Giacomo and De Vito, Ernesto and Rosasco, Lorenzo},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {22260--22269},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/vigogna22a/vigogna22a.pdf},
url = {https://proceedings.mlr.press/v162/vigogna22a.html},
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.}
}