Neural networks and the bias/variance dilemma
Neural Computation
Negative correlation learning and evolutionary design of neural network ensembles
Negative correlation learning and evolutionary design of neural network ensembles
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Managing Diversity in Regression Ensembles
The Journal of Machine Learning Research
Linear combiners for classifier fusion: some theoretical and experimental results
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Managing Diversity in Regression Ensembles
The Journal of Machine Learning Research
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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.