We study how the error of an ensemble regression estimator can be decomposed into two components: one accounting for the individual errors and the other accounting for the correlations within the ensemble. This is the well known Ambiguity decomposition; we show an alternative way to decompose the error, and show how both decompositions have been exploited in a learning scheme. Using a scaling parameter in the decomposition we can blend the gradient (and therefore the learning process) smoothly between two extremes, from concentrating on individual accuracies and ignoring diversity, up to a full non-linear optimization of all parameters, treating the ensemble as a single learning unit. We demonstrate how this also applies to ensembles using a soft combination of posterior probability estimates, so can be utilised for classifier ensembles.
|Number of pages||10|
|Journal||Lecture Notes in Computer Science|
|Publication status||Published - 1 Jan 2005|
|Event||6th International Workshop on Multiple Classifier Systems - |
Duration: 1 Jan 2005 → …