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What is role of ensembles in using artificial neural networks?
Ensemble learning combines the predictions from multiple neural network models to reduce the variance of predictions and reduce generalization error. Techniques for ensemble learning can be grouped by the element that is varied, such as training data, the model, and how predictions are combined.
Why do we need ensembles?
Ensembles are predictive models that combine predictions from two or more other models. A minimum benefit of using ensembles is to reduce the spread in the average skill of a predictive model. A key benefit of using ensembles is to improve the average prediction performance over any contributing member in the ensemble.
How are neural networks used in ensemble learning?
This stochastic nature of the learning algorithm means that each time a neural network model is trained, it may learn a slightly (or dramatically) different version of the mapping function from inputs to outputs, that in turn will have different performance on the training and holdout datasets.
How are neural networks combined in different ways?
There are N network classifiers which are combined at the top in different ways. In this example, we select 3 different classifiers to operate ensemble. They are pre-trained models on a different dataset but that we reuse in our task.
How are covariance matrices calculated in ensemble neural networks?
The covariance matrices are calculated by the ensemble randomized maximum likelihood algorithm (EnRML), which is an inverse modeling method. The ENN is able to simultaneously provide estimations and perform uncertainty quantification since it is built under the Bayesian framework.
How is ensemble learning used to make predictions?
Generally, ensemble learning involves training more than one network on the same dataset, then using each of the trained models to make a prediction before combining the predictions in some way to make a final outcome or prediction.