Genetic Programming for Kernel-Based Learning with Co-evolving Subsets Selection
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Gagne:PPSN:2006,
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author = "Christian Gagne and Marc Schoenauer and
Michele Sebag and Marco Tomassini",
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title = "Genetic Programming for Kernel-Based Learning with
Co-evolving Subsets Selection",
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booktitle = "Parallel Problem Solving from Nature - PPSN IX",
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year = "2006",
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editor = "Thomas Philip Runarsson and Hans-Georg Beyer and
Edmund Burke and Juan J. Merelo-Guervos and
L. Darrell Whitley and Xin Yao",
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volume = "4193",
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pages = "1008--1017",
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series = "LNCS",
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address = "Reykjavik, Iceland",
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publisher_address = "Berlin",
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month = "9-13 " # sep,
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publisher = "Springer-Verlag",
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ISBN = "3-540-38990-3",
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keywords = "genetic algorithms, genetic programming,
hyperheuristic, DSS, coevolution, open beagle",
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URL = "http://ppsn2006.raunvis.hi.is/proceedings/287.pdf",
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URL = "http://arxiv.org/abs/cs/0611135",
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DOI = "doi:10.1007/11844297_102",
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size = "10 pages",
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abstract = "Support Vector Machines (SVMs) are well-established
Machine Learning (ML) algorithms. They rely on the fact
that i) linear learning can be formalised as a
well-posed optimisation problem; ii) nonlinear learning
can be brought into linear learning thanks to the
kernel trick and the mapping of the initial search
space onto a high dimensional feature space. The kernel
is designed by the ML expert and it governs the
efficiency of the SVM approach. In this paper, a new
approach for the automatic design of kernels by Genetic
Programming, called the Evolutionary Kernel Machine
(EKM), is presented. EKM combines a well-founded
fitness function inspired from the margin criterion,
and a co-evolution framework ensuring the computational
scalability of the approach. Empirical validation on
standard ML benchmark demonstrates that EKM is
competitive using state-of-the-art SVMs with tuned
hyper-parameters.",
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notes = "PPSN-IX
evolved Kernels are forced to be symmetric functions.
Mercer's condition not enforced, but evolved. 3
co-evolving populations. runtime < 1 hour. Size based
parsimony pressure. Comparison with k-nn nearest
neighbours and SVM, GK-SVM (both with somewhat
optimised parameters). 6 undemanding UCI benchmarks.",
- }
Genetic Programming entries for
Christian Gagne
Marc Schoenauer
Michele Sebag
Marco Tomassini
Citations