Improving Robustness of Multiple-Objective Genetic Programming for Object Detection
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{conf/ausai/HuntJZ11,
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author = "Rachel Hunt and Mark Johnston and Mengjie Zhang",
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title = "Improving Robustness of Multiple-Objective Genetic
Programming for Object Detection",
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booktitle = "Proceedings of the 24th Australasian Joint Conference
Advances in Artificial Intelligence (AI 2011)",
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year = "2011",
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editor = "Dianhui Wang and Mark Reynolds",
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volume = "7106",
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series = "Lecture Notes in Computer Science",
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pages = "311--320",
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address = "Perth, Australia",
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month = dec # " 5-8",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-25831-2",
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DOI = "doi:10.1007/978-3-642-25832-9_32",
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size = "10 pages",
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abstract = "Object detection in images is inherently imbalanced
and prone to overfitting on the training set. This work
investigates the use of a validation set and sampling
methods in Multi-Objective Genetic Programming (MOGP)
to improve the effectiveness and robustness of object
detection in images. Results show that sampling methods
decrease run times substantially and increase
robustness of detectors at higher detection rates, and
that a combination of validation together with sampling
improves upon a validation-only approach in
effectiveness and efficiency.",
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bibdate = "2011-12-02",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ausai/ausai2011.html#HuntJZ11",
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affiliation = "School of Mathematics, Statistics and Operations
Research, Victoria University of Wellington, PO Box
600, Wellington, New Zealand",
- }
Genetic Programming entries for
Rachel Hunt
Mark Johnston
Mengjie Zhang
Citations