Using SVMs with randomised feature spaces: an extreme learning approach

Extreme learning machines are fast models which almost compare to standard SVMs in terms of accuracy, but are much faster. However, they optimise a sum of squared errors whereas SVMs are maximum-margin classifiers. This paper proposes to merge both approaches by defining a new kernel. This kernel is computed by the first layer of an extreme learning machine and used to train a SVM. Experiments show that this new kernel compares to the standard RBF kernel in terms of accuracy and is faster. Indeed, experiments show that the number of neurons of the ELM behind the randomised kernel does not need to be tuned and can be set to a sufficient value without altering the accuracy significantly.