PUBLICATION DATE

2013-03-23

AUTHORS

Guang-Bin Huang, Weiwei Zong

TITLE

Learning to Rank with Extreme Learning Machine

ISSUE

2

VOLUME

39

ISSN (print)

1370-4621

ISSN (electronic)

1573-773X

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.

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34 TRIPLES      30 PREDICATES      35 URIs      22 LITERALS

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2 sg:articleType OriginalPaper
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4 sg:coverYearMonth 2014-04
5 sg:ddsId s11063-013-9295-8
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19 sg:issnElectronic 1573-773X
20 sg:issnPrint 1370-4621
21 sg:issue 2
22 sg:language English
23 sg:license http://scigraph.springernature.com/explorer/license/
24 sg:pageEnd 166
25 sg:pageStart 155
26 sg:publicationDate 2013-03-23
27 sg:publicationYear 2013
28 sg:publicationYearMonth 2013-03
29 sg:scigraphId 0c3d2d3a49bcc10e1c2908d7fbc5590b
30 sg:title Learning to Rank with Extreme Learning Machine
31 sg:volume 39
32 sg:webpage https://link.springer.com/10.1007/s11063-013-9295-8
33 rdf:type sg:Article
34 rdfs:label Article: Learning to Rank with Extreme Learning Machine
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