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It searches for accurate neural models, characterised by good generalisation capabilities.
This fact improve the network generalisation capabilities and speed up the convergence of the learning process.
The development process of neural networks for classification problems and the influence of network architecture on its prediction and generalisation capabilities are analysed.
In this work we propose an algorithm to evolve a robust speech representation, using a dynamic data selection method for reducing the computational cost of the fitness computation while improving the generalisation capabilities.
It is well known that training machine learning classifiers with a large number of features can lead to classifier overfitting, reducing the generalisation capabilities of the classifiers and slowing down the training process.
This can be explained by the generalisation capabilities of SVC-lin in circumstances that may be similar to those presented in our work, that is, the Gaussian kernel may over-fit the training set data to yield an SVM model that is not robust (Jorissen and Gilson, 2005).
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An inter-database evaluation has been performed to investigate the generalisation capability of the proposed approach.
Stacked neural networks have enhanced model generalisation capability and can also provide model prediction confidence bounds.
Moreover, the investigated models have also been evaluated in terms of accuracy, required parameters, generalisation capability and complexity.
Moreover, a leave-one-out test was performed among the different tests, confirming the regression model generalisation capability.
Such a model, with physical a priori information embedded in the model structure, provides excellent generalisation capability and a real-time application of the model is easily possible.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com