Benchmarking manifold learning methods on a large collection of datasets
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Orzechowski:2020:EuroGP,
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author = "Patryk Orzechowski and Franciszek Magiera and
Jason Moore",
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title = "Benchmarking manifold learning methods on a large
collection of datasets",
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booktitle = "EuroGP 2020: Proceedings of the 23rd European
Conference on Genetic Programming",
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year = "2020",
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month = "15-17 " # apr,
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editor = "Ting Hu and Nuno Lourenco and Eric Medvet",
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series = "LNCS",
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volume = "12101",
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publisher = "Springer Verlag",
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address = "Seville, Spain",
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pages = "135--150",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming, Manifold
learning, Machine learning, Dimensionality reduction,
Benchmarking",
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isbn13 = "978-3-030-44093-0",
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broken = "https://www.youtube.com/watch?v=LhIkDj8oKOI",
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DOI = "doi:10.1007/978-3-030-44094-7_9",
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abstract = "Manifold learning, a non-linear approach of
dimensionality reduction, assumes that the
dimensionality of multiple datasets is artificially
high and a reduced number of dimensions is sufficient
to maintain the information about the data. a large
scale comparison of manifold learning techniques is
performed for the task of classification. We show the
current standing of genetic programming (GP) for the
task of classification by comparing the classification
results of two GP-based manifold leaning methods:
GP-Mal and ManiGP - an experimental manifold learning
technique proposed in this paper. We show that GP-based
methods can more effectively learn a manifold across a
set of 155 different problems and deliver more
separable embeddings than many established methods.",
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notes = "http://www.evostar.org/2020/cfp_eurogp.php Part of
\cite{Hu:2020:GP} EuroGP'2020 held in conjunction with
EvoCOP2020, EvoMusArt2020 and EvoApplications2020",
- }
Genetic Programming entries for
Patryk Orzechowski
Franciszek Magiera
Jason H Moore
Citations