What are the advantages of Artificial Neural Networks over Support Vector Machines?

Both Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) are supervised machine learning classifiers. An ANN is a parametric classifier that uses hyper-parameters tuning during the training phase. An SVM is a non-parametric classifier that finds a linear vector (if a linear kernel is used) to separate classes. Actually, in terms of the model performance, SVMs are sometimes equivalent to a shallow neural network architecture. Generally, an ANN will outperform an SVM when there is a large number of training instances, however, neither outperforms the other over the full range of problems.

We can summarize the advantages of the ANN over the SVM as follows:
ANNs can handle multi-class problems by producing probabilities for each class. In contrast, SVMs handle these problems using independent one-versus-all classifiers where each produces a single binary output. For example, a single ANN can be trained to solve the hand-written digits problem while 10 SVMs (one for each digit) are required.

Another advantage of ANNs, from the perspective of model size, is that the model is fixed in terms of its inputs nodes, hidden layers, and output nodes; in an SVM, however, the number of support vector lines could reach the number of instances in the worst case.

The SVM does not perform well when the number of features is greater than the number of samples. More work in feature engineering is required for an SVM than that needed for a multi-layer Neural Network.

On the other hand, SVMs are better than ANNs in certain respects:

In comparison to SVMs, ANNs are more prone to becoming trapped in local minima, meaning that they sometime miss the global picture.

While most machine learning algorithms can overfit if they don’t have enough training samples, ANNs can also overfit if training goes on for too long - a problem that SVMs do not have.

SVM models are easier to understand. There are different kernels that provide a different level of flexibilities beyond the classical linear kernel, such as the Radial Basis Function kernel (RBF). Unlike the linear kernel, the RBF can handle the case when the relation between class labels and attributes is nonlinear.

For a review of the practical results, the following link to benchmarks of the performance of SVMs and ANNs on a variety datasets is useful: