Abstract and Applied Analysis

Regularized Ranking with Convex Losses and ${\ell }^{1}$-Penalty

Abstract

In the ranking problem, one has to compare two different observations and decide the ordering between them. It has received increasing attention both in the statistical and machine learning literature. This paper considers ${\ell }^{1}$-regularized ranking rules with convex loss. Under some mild conditions, a learning rate is established.

Article information

Source
Abstr. Appl. Anal., Volume 2013, Special Issue (2013), Article ID 927827, 8 pages.

Dates
First available in Project Euclid: 26 February 2014

https://projecteuclid.org/euclid.aaa/1393450309

Digital Object Identifier
doi:10.1155/2013/927827

Mathematical Reviews number (MathSciNet)
MR3139446

Zentralblatt MATH identifier
07095500

Citation

Chen, Heng; Wu, Jitao. Regularized Ranking with Convex Losses and ${\ell }^{1}$ -Penalty. Abstr. Appl. Anal. 2013, Special Issue (2013), Article ID 927827, 8 pages. doi:10.1155/2013/927827. https://projecteuclid.org/euclid.aaa/1393450309

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