Approximation analysis of gradient descent algorithm for bipartite ranking

DOI10.1155/2012/189753zbMath1251.90314OpenAlexW2087890465WikidataQ58905860 ScholiaQ58905860MaRDI QIDQ1760585

Fangchao He, Zhibin Pan, Hong Chen

Publication date: 15 November 2012

Published in: Journal of Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2012/189753

zbMATH Keywords

gradient descent algorithmbipartite rankinglearning rates

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Sensitivity, stability, parametric optimization (90C31) Approximation methods and heuristics in mathematical programming (90C59) Combinatorial optimization (90C27)

Related Items

Randomized smoothing variance reduction method for large-scale non-smooth convex optimization

Cites Work

• Learning rates of multi-kernel regularized regression
• Gradient learning in a classification setting by gradient descent
• Ranking and empirical minimization of \(U\)-statistics
• Learning gradients by a gradient descent algorithm
• Online learning algorithms
• Learning rates of gradient descent algorithm for classification
• Shannon sampling. II: Connections to learning theory
• Learning theory estimates via integral operators and their approximations
• Online Regularized Classification Algorithms
• Statistical Analysis of Bayes Optimal Subset Ranking
• ONLINE LEARNING WITH MARKOV SAMPLING
• 10.1162/1532443041827916
• Learning Theory
• Theory of Reproducing Kernels
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item