Title

Comments on “A Parallel Mixture of SVMs for Very Large Scale Problems”

Document Type

Article

Publication Date

2004

Digital Object Identifier (DOI)

https://doi.org/10.1162/089976604323057416

Abstract

Collobert, Bengio, and Bengio (2002) recently introduced a novel approach to using a neural network to provide a class prediction from an ensemble of support vector machines (SVMs). This approach has the advantage 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 computationally efficient. This result is somewhat surprising and illustrates the general value of experimental comparisons using different types of classifiers.

Was this content written or created while at USF?

Yes

Citation / Publisher Attribution

Neural Computation, v. 16, issue 7, p. 1345-1351

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