NOTE Communicated by John Platt Comments on “A Parallel Mixture of SVMs for Very Large Scale Problems”

Collobert, Bengio, and Bengio (2002) recently introduced a novel ap-proach to using a neural network to provide a class prediction from an ensemble of support vector machines (SVMs). This approach has the ad-vantage that the required computation scales well to very large data sets. Experiments on the Forest Cover data set show that this parallel mixture is more accurate than a single SVM, with 90.72 % accuracy reported on an independent test set. Although this accuracy is impressive, their article does not consider alternative types of classifiers. We show that a simple ensemble of decision trees results in a higher accuracy, 94.75%, and is com-putationally efficient. This result is somewhat surprising and illustrates the general value of experimental comparisons using different types of classifiers.