Noise in Classification

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...

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Bibliographic Details
Main Authors Balcan, Maria-Florina, Haghtalab, Nika
Format Journal Article
LanguageEnglish
Published 10.10.2020
Subjects
Computer Science - Data Structures and Algorithms
Computer Science - Learning
Statistics - Machine Learning
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Summary: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.
DOI:10.48550/arxiv.2010.05080