Learning regression ensembles with genetic programming at scale
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Veeramachaneni:2013:GECCO,
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author = "Kalyan Veeramachaneni and Owen Derby and
Dylan Sherry and Una-May O'Reilly",
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title = "Learning regression ensembles with genetic programming
at scale",
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booktitle = "GECCO '13: Proceeding of the fifteenth annual
conference on Genetic and evolutionary computation
conference",
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year = "2013",
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editor = "Christian Blum and Enrique Alba and Anne Auger and
Jaume Bacardit and Josh Bongard and Juergen Branke and
Nicolas Bredeche and Dimo Brockhoff and
Francisco Chicano and Alan Dorin and Rene Doursat and
Aniko Ekart and Tobias Friedrich and Mario Giacobini and
Mark Harman and Hitoshi Iba and Christian Igel and
Thomas Jansen and Tim Kovacs and Taras Kowaliw and
Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and
John McCall and Alberto Moraglio and
Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and
Gustavo Olague and Yew-Soon Ong and
Michael E. Palmer and Gisele Lobo Pappa and
Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and
Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and
Daniel Tauritz and Leonardo Vanneschi",
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isbn13 = "978-1-4503-1963-8",
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pages = "1117--1124",
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keywords = "genetic algorithms, genetic programming",
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month = "6-10 " # jul,
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organisation = "SIGEVO",
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address = "Amsterdam, The Netherlands",
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DOI = "doi:10.1145/2463372.2463506",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "In this paper we examine the challenge of producing
ensembles of regression models for large datasets. We
generate numerous regression models by concurrently
executing multiple independent instances of a genetic
programming learner. Each instance may be configured
with different parameters and a different subset of the
training data. Several strategies for fusing
predictions from multiple regression models are
compared. To overcome the small memory size of each
instance, we challenge our framework to learn from
small subsets of training data and yet produce a
prediction of competitive quality after fusion. This
decreases the running time of learning which produces
models of good quality in a timely fashion. Finally, we
examine the quality of fused predictions over the
progress of the computation.",
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notes = "Also known as \cite{2463506} GECCO-2013 A joint
meeting of the twenty second international conference
on genetic algorithms (ICGA-2013) and the eighteenth
annual genetic programming conference (GP-2013)",
- }
Genetic Programming entries for
Kalyan Veeramachaneni
Owen C Derby
Dylan Sherry
Una-May O'Reilly
Citations