Linear Genetic Programming for Multi-class Object Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{conf/ausai/FogelbergZ05,
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title = "Linear Genetic Programming for Multi-class Object
Classification",
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author = "Christopher Fogelberg and Mengjie Zhang",
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year = "2005",
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bibdate = "2005-11-29",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ausai/ausai2005.html#FogelbergZ05",
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pages = "369--379",
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booktitle = "AI 2005: Advances in Artificial Intelligence, 18th
Australian Joint Conference on Artificial Intelligence,
Proceedings",
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editor = "Shichao Zhang and Ray Jarvis",
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publisher = "Springer",
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series = "Lecture Notes in Computer Science",
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volume = "3809",
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address = "Sydney, Australia",
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month = dec # " 5-9",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "3-540-30462-2",
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DOI = "doi:10.1007/11589990_39",
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size = "11 pages",
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abstract = "Multi-class object classification is an important
field of research in computer vision. In this paper
basic linear genetic programming is modified to be more
suitable for multi-class classification and its
performance is then compared to tree-based genetic
programming. The directed acyclic graph nature of
linear genetic programming is exploited. The existing
fitness function is modified to more accurately
approximate the true feature space. The results show
that the new linear genetic programming approach
outperforms the basic tree-based genetic programming
approach on all the tasks investigated here and that
the new fitness function leads to better and more
consistent results. The genetic programs evolved by the
new linear genetic programming system are also more
comprehensible than those evolved by the tree-based
system.",
- }
Genetic Programming entries for
Christopher Fogelberg
Mengjie Zhang
Citations