Lifting the Curse of Dimensionality
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InCollection{Worzel:2006:GPTP,
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author = "W. P. Worzel and A. Almal and C. D. MacLean",
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title = "Lifting the Curse of Dimensionality",
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booktitle = "Genetic Programming Theory and Practice {IV}",
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year = "2006",
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editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
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volume = "5",
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series = "Genetic and Evolutionary Computation",
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pages = "29--40",
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address = "Ann Arbor",
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month = "11-13 " # may,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "0-387-33375-4",
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DOI = "doi:10.1007/978-0-387-49650-4_3",
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abstract = "In certain problem domains the 'Curse of
Dimensionality' [Hastie et al., 2001] is well known.
Also known as the problem of 'High P and Low N' where
the number of parameters far exceeds the number of
samples to learn from, we describe our methods for
making the most of limited samples in producing
reasonably general classification rules from data with
a larger number of parameters. We discuss the
application of this approach in classifying
mesothelioma samples from baseline data according to
their time to recurrence. In this case there are over
12625 inputs for each sample but only 19 samples to
learn from. We reflect on the theoretical implications
of the behaviour of GP in these extreme cases and
speculate on the nature of generality.",
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notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007
after the workshop",
- }
Genetic Programming entries for
William P Worzel
Arpit A Almal
Duncan MacLean
Citations