Ensemble Learning in Linearly Combined Classifiers Via Negative Correlation
نویسندگان
چکیده
We investigate the theoretical links between a regression ensemble and a linearly combined classification ensemble. First, we reformulate the Tumer & Ghosh model for linear combiners in a regression context; we then exploit this new formulation to generalise the concept of the “Ambiguity decomposition”, previously defined only for regression tasks, to classification problems. Finally, we propose a new algorithm, based on the Negative Correlation Learning framework, which applies to ensembles of linearly combined classifiers.
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