[signa11]

> I can't believe I'm getting down-voted just because I'm not bullish on ANN.

probably it is happening because ANN's definitely do have some advantage over SVM's for modelling real world phenomenon.

specifically, ANN's are _parametric_, while SVM's are nonparametric in the sense that for an ANN, you have a bunch of hidden layers (of varying sizes) depending on the number of features, and a bias parameter. this is your model.

SVM's otoh (at least in the kernelized case), consist of set of support-vectors selected from a training set. which in worst case can be as large as the training set.

modelling real world phenomenon, for example optimal air-conditioning based on a large number of external inputs in a data center, are far more conducive in ANN's than SVM's. ANN's are afterall universal approximators. with SVM's you have to guess the kernel...

edit-001 : see for example, this paper: http://deeplearning.net/wp-content/uploads/2013/03/dlsvm.pdf , where folks try their hands at deep-learning via SVM's.