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Where is ANN classification (regression) better than SVM? Some real-world examples?
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The difference is mainly on how non-linear data is classified. Basically, SVM utilizes nonlinear mapping to make the data linear separable, hence the kernel function is the key. However, ANN employs multi-layer connection and various activation functions to deal with nonlinear problems.
Practically, mostly no. For most modern problems DNNs are a better choice. If your input data size is small and you are successful in finding a suitable kernel, however, an SVM may be a more efficient solution. But, if you can't determine a suitable kernel, NNs are then a better choice.
1) SVMs are not suitable for large datasets The original SVM implementation is known to have a concrete theoretical foundation, but it is not suitable for classifying in large datasets for one straightforward reason — the complexity of the algorithm's training is highly dependent on the size of the dataset.
In this article, we studied the advantages of ANNs against SVMs, and vice versa. We tested the two models against three datasets that we used as benchmarks. In doing so, we learned that, in support of the theoretical expectations, training time for neural networks is significantly slower than training time for SVMs.
There are many applications where they're better, many applications where they're comparable, many applications where they are worse. It also depends on who you ask. It is hard to say this type of data or that type of data/application.
An example where ANN, in particular convolutional neural networks, work better than SVMs would be digit classification on MNIST. Another such case is the work of Geoff Hinton's group on speech recognition using Deep Belief Networks
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