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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.
Preference learning can be used in ranking search results according to feedback of user preference. Given a query and a set of documents, a learning model is used to find the ranking of documents corresponding to the relevance with this query. More discussions on research in this field can be found in Tie-Yan Liu's survey paper. [6]
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.
It was also applied in machine learning and information retrieval. [18] It was also applied in social choice, to analyze an opinion poll conducted during the Irish presidential election. [19] Efficient methods for expectation-maximization and Expectation propagation exist for the Plackett-Luce model. [20] [21] [22]
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.
Download as PDF; Printable version; ... Feature learning; Learning to rank; Grammar induction; ... It contained over 7,000 unpublished fiction books from various ...
Michael R. Lyu is the Choh-Ming Li Professor of Computer Science and Engineering at the Chinese University of Hong Kong in Shatin, Hong Kong.Lyu is well known to the software engineering community as the editor of two classic book volumes in software reliability engineering: Software Fault Tolerance [1] and the Handbook of Software Reliability Engineering. [2]
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