… method for combining classifiers that uses estimates of each individual classifier’s local accuracy
in … An empirical evaluation using five real data sets confirms the validity of our approach …

… The simultaneous minimization is either approached as a multi-objectiveproblem or with
aggregating functions combining mean andDesign SpaceParameter Min Max -n128 -n64 -n32 -…

… We set MFS's subset size parameter based on crossvalidation accuracy estimates on the
training set for the entire ensemble. We evaluated ten evenly spaced intervals over the size of …

… , we use local classifier accuracy estimates to weight classifier … , we estimate local recognition
accuracies of classifiers near a … , “Combination of multiple classifiers using local accuracy …

… , the product rule, for combining multiple classification rules, and I … regression method of
combining classification rules. To … accuracy estimates for the three combination classifiers and …

… To begin with, we discuss several related combination … After introducing the local within-class
accuracy method, we conduct an … classifier selection by means of local accuracy estimates. …

… more accurate approach that relies on continuous valued scores obtained from local features.
… of our approach, by describing how posterior estimates are obtained from a local feature. …

… are presented that use estimates of each individual classifier’s local accuracy in small …
For more generic reviews on multiple classifier systems and combining classifiers the reader is …

… in determining the overall accuracy of the classifier combinations. Therefore, to minimize the
… It involves the weighting of classifiers based on their estimated recognition performance, to …

… combining multiple classifiers is based on the observation that achieving optimal performance
in combination … incorporation of bootstrapping for estimating the measures enables good …