University of Surrey

Test tubes in the lab Research in the ATI Dance Research

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.

[img]
Preview
PDF
PID1301583.pdf - Presentation
Available under License : See the attached licence file.

Download (126kB)
[img] Plain Text (licence)
licence.txt

Download (1kB)

Abstract

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 (Paper)
Divisions: Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Depositing User: Symplectic Elements
Date Deposited: 19 Sep 2011 13:06
Last Modified: 23 Sep 2013 18:44
URI: http://epubs.surrey.ac.uk/id/eprint/7130

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800