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(Those models that combined economic and polling data have had considerably better results).
Those models that combined nest-site and landscape scales improved the predictive ability compared with the scale-specific ones.
It was found that the statistical models that combined the tree-ring reconstructed data with the observed data outperformed those that did not, in terms of reducing the root mean squared errors.
Lee et al. 1996 developed hybrid neural network models that combined statistical techniques (i.e., multi-variate discriminant analysis (MDA) and the ID3 method with neural networks, or combined two different neural networks to predict corporate bankruptcy, and suggested that hybrid neural network models were potentially effective models for bankruptcy prediction.
Economists incorporated the theoretical work from the synthesis into large-scale macroeconometric models that combined individual equations for factors such as consumption, investment, and money demand with empirically observed data.
To evaluate the contribution of each feature, we tested CTF models that combined different features.
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Probabilistic Relational Models (PRMs Getoorr et al., 2002 ) and their extension ProBic (Van den Bulcke, 2009 ) are fully generative models that combine probabilistic modeling and relational logic.
With the continuous deepening of research, hybrid models that combine several methods have been developed.
Models that combine mass balance with bioindicators can be called 'effect-load-sensitivity' models.
Groundwater management models that combine optimization methods with coupled stream/aquifer simulation models are considered.
Recently developed hybrid models that combine different machine learning techniques have shown promising results.
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