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
Scholar Commons Citation
Liu, Xiaomei; Hall, Lawrence O.; and Bowyer, Kevin W., "Comments on “A Parallel Mixture of SVMs for Very Large Scale Problems”" (2004). Computer Science and Engineering Faculty Publications. 132.
https://digitalcommons.usf.edu/esb_facpub/132
