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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:

ANN and SVM classification

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).

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It is assumed that by ANNs, you intend multilayer feed-forward networks, such as multilayer perceptrons because those are in close competition with SVMs.Here are the advantages of using ANN over SVM:

1. One explicit advantage of using these models over SVMs is that their size is fixed. 

2.SVM's are non-parametric and they are parametric models. It means that in an ANN there is a collection of deep hidden layers with sizes h1 through hn depending on the number of characteristics and bias parameters. 

3.SVM consists of a collection of support vectors, chosen from the training set, with a weight for each. In the worst case, the number of support vectors is precisely the number of training samples and usually, its model size scales linearly. 

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