A Bias-Variance Analysis of Bootstrapped Class-Separability Weighting for Error-Correcting Output Code Ensembles
Smith, RS and Windeatt, T (2010) A Bias-Variance Analysis of Bootstrapped Class-Separability Weighting for Error-Correcting Output Code Ensembles In: 22nd International Conference on Pattern Recognition (ICPR), 2010-08-23 - 2010-08-26, Istanbul, Turkey.
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We investigate the effects, in terms of a bias-variance decomposition of error, of applying class-separability weighting plus bootstrapping in the construction of error-correcting output code ensembles of binary classifiers. Evidence is presented to show that bias tends to be reduced at low training strength values whilst variance tends to be reduced across the full range. The relative importance of these effects, however, varies depending on the stability of the base classifier type.
|Item Type:||Conference or Workshop Item (Conference Paper)|
|Divisions :||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Date :||23 August 2010|
|Depositing User :||Symplectic Elements|
|Date Deposited :||19 Sep 2011 13:06|
|Last Modified :||23 Sep 2013 18:44|
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