Support Vector Machines versus Neural Networks

“Interest in neural networks appears to have declined since the arrival of support vector machines, perhaps because the latter generally require fewer parameters to be tuned to achieve the same (or greater) accuracy. However, multilayer perceptrons have the advantage that they can learn to ignore irrelevant attributes, and RBF networks trained using k-means can be viewed as a quick-and-dirty method for finding a nonlinear classifier.” (p. 235)

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