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Ensemble Methods in Machine Learning

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If you have a question about this talk, please contact Konstantina Palla.

In this talk I will give an overview of the field of ensemble methods.These techniques induce a collection (ensemble) of predictors and then combine their output into a final consensus response which is usually more accurate than the output of the best individual predictor. The aggregation process often generates a reduction in the bias and/or the variance components of the error of the final system. An important factor in the implementation of ensemble methods is to select the optimal size of the ensemble. Over-estimation of this parameter can result in a waste of resources while under-estimation can result in loss of prediction accuracy. The last part of this talk will describe a method for selecting the optimal size of a parallel ensemble of binary classifiers.

This talk is part of the Machine Learning Reading Group @ CUED series.

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