Performance measures of the tomographic classifier fusion methodology
Windridge, D and Kittler, J (2005) Performance measures of the tomographic classifier fusion methodology Int. Journal of Pattern Recognition and Artificial Intelligence, 19 (6). pp. 731-753.
We seek to quantify both the classification performance and estimation error robustness of the authors' tomographic classifier fusion methodology by contrasting it in field tests and model scenarios with the sum and product classifier fusion methodologies. In particular, we seek to confirm that the tomographic methodology represents a generally optimal strategy across the entire range of problem dimensionalities, and at a sufficient margin to justify the general advocation of its use. Final results indicate, in particular, a near 25% improvement on the next nearest performing combination scheme at the extremity of the tested dimensional range.
|Divisions :||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Date :||1 September 2005|
|Identification Number :||https://doi.org/10.1142/S0218001405004319|
|Additional Information :||Electronic version of an article published in Int. Journal of Pattern Recognition and Artificial Intelligence, Volume 19, Issue 6, 2005, pp. 731-753 DOI: 10.1142/S0218001405004319 © copyright World Scientific Publishing Company. http://www.worldscientific.com/loi/ijprai|
|Depositing User :||Symplectic Elements|
|Date Deposited :||18 Sep 2013 08:31|
|Last Modified :||23 Sep 2013 20:17|
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