Visual Learning by Evolutionary Feature Synthesis
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{KrawiecBhanu03,
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author = "Krzysztof Krawiec and Bir Bhanu",
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title = "Visual Learning by Evolutionary Feature Synthesis",
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booktitle = "Proceedings of the Twentieth International Conference
on Machine Learning ({ICML} 2003)",
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year = "2003",
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editor = "Tom Fawcett and Nina Mishra",
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pages = "376--383",
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address = "Washington, DC, USA",
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month = aug # " 21-24",
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publisher = "AAAI Press",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "1-57735-189-4",
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URL = "http://www.aaai.org/Library/ICML/2003/icml03-051.php",
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URL = "http://www.aaai.org/Papers/ICML/2003/ICML03-051.pdf",
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size = "8 pages",
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abstract = "In this paper, we present a novel method for learning
complex concepts/hypotheses directly from raw training
data. The task addressed here concerns data-driven
synthesis of recognition procedures for real-world
object recognition task. The method uses linear genetic
programming to encode potential solutions expressed in
terms of elementary operations, and handles the
complexity of the learning task by applying cooperative
coevolution to decompose the problem automatically. The
training consists in coevolving feature extraction
procedures, each being a sequence of elementary image
processing and feature extraction operations. Extensive
experimental results show that the approach attains
competitive performance for 3-D object recognition in
real synthetic aperture radar (SAR) imagery.",
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notes = "Also known as \cite{DBLP:conf/icml/KrawiecB03} ICML
2003",
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bibsource = "DBLP, http://dblp.uni-trier.de",
- }
Genetic Programming entries for
Krzysztof Krawiec
Bir Bhanu
Citations