ANN (Artificial Neural Networks) and SVM (Support Vector Machines) are two popular strategies for supervised machine learning and classification. It's not often clear which method is better for a particular project, and I'm certain the answer is always "it depends." Often, a combination of both along with Bayesian classification is used.
These questions on Stackoverflow have already been asked regarding ANN vs SVM:
what the difference among ANN, SVM and KNN in my classification question
Support Vector Machine or Artificial Neural Network for text processing?
In this question, I'd like to know specifically what aspects of an ANN (specifically, a Multilayer Perceptron) might make it desirable to use over an SVM? The reason I ask is because it's easy to answer the opposite question: Support Vector Machines are often superior to ANNs because they avoid two major weaknesses of ANNs:
(1) ANNs often converge on local minima rather than global minima, meaning that they are essentially "missing the big picture" sometimes (or missing the forest for the trees)
(2) ANNs often overfit if training goes on too long, meaning that for any given pattern, an ANN might start to consider the noise as part of the pattern.
SVMs don't suffer from either of these two problems. However, it's not readily apparent that SVMs are meant to be a total replacement for ANNs. So what specific advantage(s) does an ANN have over an SVM that might make it applicable for certain situations? I've listed specific advantages of an SVM over an ANN, now I'd like to see a list of ANN advantages (if any).
This question is related to
Judging from the examples you provide, I'm assuming that by ANNs, you mean multilayer feed-forward networks (FF nets for short), such as multilayer perceptrons, because those are in direct competition with SVMs.
One specific benefit that these models have over SVMs is that their size is fixed: they are parametric models, while SVMs are non-parametric. That is, in an ANN you have a bunch of hidden layers with sizes h1 through hn depending on the number of features, plus bias parameters, and those make up your model. By contrast, an SVM (at least a kernelized one) consists of a set of support vectors, selected from the training set, with a weight for each. In the worst case, the number of support vectors is exactly the number of training samples (though that mainly occurs with small training sets or in degenerate cases) and in general its model size scales linearly. In natural language processing, SVM classifiers with tens of thousands of support vectors, each having hundreds of thousands of features, is not unheard of.
Also, online training of FF nets is very simple compared to online SVM fitting, and predicting can be quite a bit faster.
EDIT: all of the above pertains to the general case of kernelized SVMs. Linear SVM are a special case in that they are parametric and allow online learning with simple algorithms such as stochastic gradient descent.