Improving Robustness of Multiple-Objective Genetic Programming for Object Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{conf/ausai/HuntJZ11,

• author = "Rachel Hunt and Mark Johnston and Mengjie Zhang",
• title = "Improving Robustness of Multiple-Objective Genetic Programming for Object Detection",
• booktitle = "Proceedings of the 24th Australasian Joint Conference Advances in Artificial Intelligence (AI 2011)",
• year = "2011",
• editor = "Dianhui Wang and Mark Reynolds",
• volume = "7106",
• series = "Lecture Notes in Computer Science",
• pages = "311--320",
• address = "Perth, Australia",
• month = dec # " 5-8",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-642-25831-2",
• DOI = "doi:10.1007/978-3-642-25832-9_32",
• size = "10 pages",
• 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.",
• bibdate = "2011-12-02",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/ausai/ausai2011.html#HuntJZ11",
• 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