Approximation analysis of gradient descent algorithm for bipartite ranking
type: Item
instance of: scholarly article
title: Approximation analysis of gradient descent algorithm for bipartite ranking
zbMATH Open document ID: 1251.90314
DOI: 10.1155/2012/189753
author: Hong Chen
author: Fangchao He
author: Zhibin Pan
published in: Journal of Applied Mathematics
publication date: 2012-11-15T00:00:00Z
review text: Summary: We introduce a gradient descent algorithm for bipartite ranking with general convex losses. The implementation of this algorithm is simple, and its generalization performance is investigated. Explicit learning rates are presented in terms of the suitable choices of the regularization parameter and the step size. The result fills the theoretical gap in learning rates for ranking problem with general convex losses.
Mathematics Subject Classification ID: 90C27
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 90C31
zbMATH DE Number: 6106136
zbMATH Keywords: gradient descent algorithm
zbMATH Keywords: bipartite ranking
zbMATH Keywords: learning rates
Wikidata QID: Q58905860
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/189753
OpenAlex ID: W2087890465
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description: scientific article; zbMATH DE number 6106136