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Published in:
2008 | OriginalPaper | Chapter
Ensemble MLP Classifier Design
Author : Terry Windeatt
Published in: Computational Intelligence Paradigms
Publisher: Springer Berlin Heidelberg
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Multi-layer perceptrons (MLP) make powerful classifiers that may provide superior performance compared with other classifiers, but are often criticized for the number of free parameters. Most commonly, parameters are set with the help of either a validation set or cross-validation techniques, but there is no guarantee that a pseudo-test set is representative. Further difficulties with MLPs include long training times and local minima. In this chapter, an ensemble of MLP classifiers is proposed to solve these problems. Parameter selection for optimal performance is performed using measures that correlate well with generalisation error.