Learning to Rank
Created by W.Langdon from
gp-bibliography.bib Revision:1.7906
- @Article{Trotman2005_Article_LearningToRank,
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author = "Andrew Trotman",
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title = "Learning to Rank",
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journal = "Information Retrieval",
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year = "2005",
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volume = "8",
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pages = "359--381",
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month = jan,
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keywords = "genetic algorithms, genetic programming, searching,
document ranking, machine learning",
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annote = "The Pennsylvania State University CiteSeerX Archives",
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bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
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language = "en",
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oai = "oai:CiteSeerX.psu:10.1.1.1042.5037",
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rights = "Metadata may be used without restrictions as long as
the oai identifier remains attached to it.",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1042.5037",
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URL = "http://ccc.inaoep.mx/%7Evillasen/bib/Trotman-lerningRank07.pdf",
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URL = "https://rdcu.be/dR76m",
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DOI = "doi:10.1007/s10791-005-6991-7",
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size = "23 pages",
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abstract = "New general purpose ranking functions are discovered
using genetic programming. The TREC WSJ collection was
chosen as a training set. A baseline comparison
function was chosen as the best of inner product,
probability, cosine, and Okapi BM25. An elitist genetic
algorithm with a population size 100 was run 13 times
for 100 generations and the best performing algorithms
chosen from these. The best learnt functions, when
evaluated against the best baseline function (BM25),
demonstrate some significant performance differences,
with improvements in mean average precision as high as
32percent observed on one TREC collection not used in
training. In no test is BM25 shown to significantly
outperform the best learnt function.",
- }
Genetic Programming entries for
Andrew Trotman
Citations