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Boosting multiple experts by joint optimisation of decision thresholds

Kittler, J, Yusoff, Y, Christmas, W, Windeatt, T and Windridge, D (2001) Boosting multiple experts by joint optimisation of decision thresholds Pattern Recognition and Image Analysis, 11, 3. pp. 529-541.

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Abstract

We consider a multiple classifier system which combines the hard decisions of experts by voting. We argue that the individual experts should not set their own decision thresholds. The respective thresholds should be selected jointly as this will allow compensation of the weaknesses of some experts by the relative strengths of the others. We perform the joint optimization of decision thresholds for a multiple expert system by a systematic sampling of the multidimensional decision threshold space. We show the effectiveness of this approach on the important practical application of video shot cut detection.

Item Type: Article
Authors :
NameEmailORCID
Kittler, JUNSPECIFIEDUNSPECIFIED
Yusoff, YUNSPECIFIEDUNSPECIFIED
Christmas, Ww.christmas@surrey.ac.ukUNSPECIFIED
Windeatt, TUNSPECIFIEDUNSPECIFIED
Windridge, Dd.windridge@surrey.ac.ukUNSPECIFIED
Date : 2001
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 11:38
Last Modified : 17 May 2017 14:57
URI: http://epubs.surrey.ac.uk/id/eprint/832184

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