Learning to Rank with Extreme Learning Machine View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-04

AUTHORS

Weiwei Zong, Guang-Bin Huang

ABSTRACT

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. More... »

PAGES

155-166

References to SciGraph publications

  • 2011-06. Extreme learning machines: a survey in INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
  • 1999-06. Least Squares Support Vector Machine Classifiers in NEURAL PROCESSING LETTERS
  • 2010-08. LETOR: A benchmark collection for research on learning to rank for information retrieval in INFORMATION RETRIEVAL JOURNAL
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11063-013-9295-8

    DOI

    http://dx.doi.org/10.1007/s11063-013-9295-8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1043864868


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