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  2. Learning to rank - Wikipedia

    en.wikipedia.org/wiki/Learning_to_rank

    Learning to rank [1] or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. [2]

  3. File:RelativeRankLearning2.pdf - Wikipedia

    en.wikipedia.org/wiki/File:RelativeRankLearning2.pdf

    English: Learning in the partial-information sequential search paradigm. The numbers display the expected values of applicants based on their relative rank (out of m total applicants seen so far) at various points in the search. Expectations are calculated based on the case when their values are uniformly distributed between 0 and 1.

  4. File:PokerHandRankings.pdf - Wikipedia

    en.wikipedia.org/wiki/File:PokerHandRankings.pdf

    You are free: to share – to copy, distribute and transmit the work; to remix – to adapt the work; Under the following conditions: attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses ...

  5. Category:Ranking functions - Wikipedia

    en.wikipedia.org/wiki/Category:Ranking_functions

    Download as PDF; Printable version; In other projects ... Pages in category "Ranking functions" ... Learning to rank; O.

  6. Category:Information retrieval techniques - Wikipedia

    en.wikipedia.org/wiki/Category:Information...

    Print/export Download as PDF; Printable version; In other projects Wikimedia Commons; ... Learning to rank; Literature-based discovery; M. Music alignment; N.

  7. Talk:Learning to rank - Wikipedia

    en.wikipedia.org/wiki/Talk:Learning_to_rank

    Talk: Learning to rank. ... Print/export Download as PDF; Printable version; In other projects Appearance. move to sidebar hide

  8. Ranking SVM - Wikipedia

    en.wikipedia.org/wiki/Ranking_SVM

    In machine learning, a ranking SVM is a variant of the support vector machine algorithm, which is used to solve certain ranking problems (via learning to rank). The ranking SVM algorithm was published by Thorsten Joachims in 2002. [1] The original purpose of the algorithm was to improve the performance of an internet search engine.

  9. Lemur Project - Wikipedia

    en.wikipedia.org/wiki/Lemur_Project

    Updates to the Lemur Project components are made twice a year, in June and December. The latest version of the Indri search engine is 5.17. The latest version of the Galago search engine is version 3.18. The latest version of the RankLib learning-to-rank library is 2.14. The latest version of the Sifaka data mining application is 1.8.