Learning rates for regularized least squares ranking algorithm

Yulong Zhao, Jun Fan*, Lei Shi

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

24 Citations (Scopus)


The ranking problem aims at learning real-valued functions to order instances, which has attracted great interest in statistical learning theory. In this paper, we consider the regularized least squares ranking algorithm within the framework of reproducing kernel Hilbert space. In particular, we focus on analysis of the generalization error for this ranking algorithm, and improve the existing learning rates by virtue of an error decomposition technique from regression and Hoeffding's decomposition for U-statistics.

Original languageEnglish
Pages (from-to)815-836
Number of pages22
JournalAnalysis and Applications
Issue number6
Publication statusPublished - 1 Nov 2017

Scopus Subject Areas

  • Analysis
  • Applied Mathematics

User-Defined Keywords

  • approximation error
  • covering number
  • generalization bound
  • Ranking algorithm
  • U-statistics


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