In negative correlation learning, all the individual networks in the ensemble are trained simulta- neously through the correlation penalty terms in their error functions. Negative correlation learning attempts to train and combine individual networks in the same learning process.
We investigate the theoretical links between a regression ensemble and a linearly combined classification ensemble. First, we reformulate the Tumer & Ghosh ...
Abstract. We investigate the theoretical links between a regression en- semble and a linearly combined classification ensemble. First, we refor-.
Ensemble Learning in Linearly Combined Classifiers Via Negative ...
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Abstract. We investigate the theoretical links between a regression en- semble and a linearly combined classification ensemble. First, we refor-.
We investigate the theoretical links between a regression ensemble and a linearly combined classification ensemble. First, we reformulate the Tumer & Ghosh ...
The experimental results show that negative correlation learning can produce neural network ensembles with good generalisation ability.
Missing: Linearly Classifiers
Both theoretical and experimental studies have shown that combining accurate neural networks (NNs) in the ensemble with negative error correlation greatly ...
In negative correlation learning, all the individual networks in the ensemble are trained simultaneously and interactively through the correlation penalty terms ...
The idea of ensemble learning is to employ multiple learners and combine their predictions. There is no definitive taxonomy. Jain, Duin and Mao (2000) list.
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance