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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]
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.
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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.
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.