Multiclass Learning with Margin: Exponential Rates with No Bias-Variance Trade-Off
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.
Cite
Text
Vigogna et al. "Multiclass Learning with Margin: Exponential Rates with No Bias-Variance Trade-Off." International Conference on Machine Learning, 2022.Markdown
[Vigogna et al. "Multiclass Learning with Margin: Exponential Rates with No Bias-Variance Trade-Off." International Conference on Machine Learning, 2022.](https://mlanthology.org/icml/2022/vigogna2022icml-multiclass/)BibTeX
@inproceedings{vigogna2022icml-multiclass,
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 = {International Conference on Machine Learning},
year = {2022},
pages = {22260-22269},
volume = {162},
url = {https://mlanthology.org/icml/2022/vigogna2022icml-multiclass/}
}