Some Training Subset Selection Methods for Supervised Learning in Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Unpublished{gathercole:1994:stss,
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author = "Chris Gathercole and Peter Ross",
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title = "Some Training Subset Selection Methods for Supervised
Learning in Genetic Programming",
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note = "Presented at ECAI'94 Workshop on Applied Genetic and
other Evolutionary Algorithms",
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year = "1994",
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keywords = "genetic algorithms, genetic programming, LEF, DSS",
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URL = "http://citeseer.ist.psu.edu/cache/papers/cs/733/ftp:zSzzSzftp.dai.ed.ac.ukzSzpubzSzuserzSzchrisgzSzchrisg_dss_paper_resubmitted_to_ecai94workshop.pdf/gathercole94some.pdf",
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URL = "http://citeseer.ist.psu.edu/gathercole94some.html",
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abstract = "When using the Genetic Programming (GP) Algorithm on a
difficult problem with a large set of training cases, a
large population size is needed and a very large number
of function-tree evaluations must be carried out. This
paper describes how to reduce the number of such
evaluations by selecting a small subset of the training
data set on which to actually carry out the GP
algorithm. Three subset selection methods described in
the paper are: Dynamic Subset Selection (DSS), using
the current...",
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size = "13 pages",
- }
Genetic Programming entries for
Chris Gathercole
Peter Ross
Citations