Four types of noise in data for PAC learning
type: Item
instance of: scholarly article
title: Four types of noise in data for PAC learning
zbMATH Open document ID: 1004.68539
DOI: 10.1016/0020-0190(95)00016-6
author: R. H. Sloan
published in: Information Processing Letters
publication date: 1997-02-28T00:00:00Z
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 985661
zbMATH Keywords: concept learning
zbMATH Keywords: design of algorithms
zbMATH Keywords: noise
zbMATH Keywords: PAC learning
zbMATH Keywords: computational learning theory
MaRDI profile type: Publication
full work available at URL: 0020-0190(95)00016-6
OpenAlex ID: W2036478832
cites work: Occam's razor
cites work: Can PAC learning algorithms tolerate random attribute noise?
cites work: Equivalence of models for polynomial learnability
cites work: Probability Inequalities for Sums of Bounded Random Variables
cites work: Efficient noise-tolerant learning from statistical queries
cites work: Learning in the Presence of Malicious Errors
cites work: Q4040311
cites work: A theory of the learnable
description: scientific article; zbMATH DE number 985661