Neural Processing Letters

, Volume 39, Issue 2, pp 155–166 | Cite as

Learning to Rank with Extreme Learning Machine

  • Weiwei ZongEmail author
  • Guang-Bin Huang


Relevance ranking has been a popular and interesting topic over the years, which has a large variety of applications. A number of machine learning techniques were successfully applied as the learning algorithms for relevance ranking, including neural network, regularized least square, support vector machine and so on. From machine learning point of view, extreme learning machine actually provides a unified framework where the aforementioned algorithms can be considered as special cases. In this paper, pointwise ELM and pairwise ELM are proposed to learn relevance ranking problems for the first time. In particular, ELM type of linear random node is newly proposed together with kernel version of ELM to be linear as well. The famous publicly available dataset collection LETOR is tested to compare ELM-based ranking algorithms with state-of-art linear ranking algorithms.


Extreme learning machine Learning to rank Linear random node Linear kernal 


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.School of Electrical and Electronic Engineering, Nanyang Technological University SingaporeSingapore

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