General bounds on statistical query learning and PAC learning with noise via hypothesis boosting
type: Item
instance of: scholarly article
title: General bounds on statistical query learning and PAC learning with noise via hypothesis boosting
zbMATH Open document ID: 0948.68160
author: Javed A. Aslam
author: Scott E. Decatur
published in: Information and Computation
publication date: 1998-11-10T00:00:00Z
full work available at URL: http://hdl.handle.net/2047/d20000320
review text: The authors have derived the general bounds on the complexity of learning in the statistical query model and in the PAC model with classification noise. By the way the authors have shown a general scheme for boosting the accuracy of weak SQ learning algorithms in order to prove that the weak of SQ learning is equivalent to strong SQ learning.
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 94-04
Mathematics Subject Classification ID: 92E10
zbMATH DE Number: 1220698
zbMATH Keywords: SQ learning algorithms
reviewed by: Laxminarayan Das
MaRDI profile type: Publication
OpenAlex ID: W2078000861
cites work: Q4004178
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DOI: 10.1006/INCO.1998.2664
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description: scientific article; zbMATH DE number 1220698