Noise in Classification
Machine Learning
2020-11-16 v2 Data Structures and Algorithms
Machine Learning
Abstract
This chapter considers the computational and statistical aspects of learning linear thresholds in presence of noise. When there is no noise, several algorithms exist that efficiently learn near-optimal linear thresholds using a small amount of data. However, even a small amount of adversarial noise makes this problem notoriously hard in the worst-case. We discuss approaches for dealing with these negative results by exploiting natural assumptions on the data-generating process.
Cite
@article{arxiv.2010.05080,
title = {Noise in Classification},
author = {Maria-Florina Balcan and Nika Haghtalab},
journal= {arXiv preprint arXiv:2010.05080},
year = {2020}
}
Comments
Chapter 16 of the book Beyond the Worst-Case Analysis of Algorithms