ANN and SVM classification

问题:

Where is ANN classification (regression) better than SVM? Some real-world examples?

回答1:

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

回答2:

Recently I have read a paper of proving the theoretical equivalence between ANN and SVM. However, ANN is usually slower than SVM.

回答3:

I am just finishing some out-of-the-box comparison between support vector machines and neural networks on several popular regression- and classification datasets - first results in short: svms learn fast and predict slow - neural networks learn slow but predict fast and have very lightweight models. Concerning accuracy/loss, both methods seem to be on par.

回答4:

It will largely depend as both have different tradeoffs and design criteria. There has been some work to show the relationship and some say equivalence as seen in other answers to this question. Below is another reference which draws links between these two techniques in machine learning:

Ronan Collobert and Samy Bengio. 2004. Links between perceptrons, MLPs and SVMs. In Proceedings of the twenty-first international conference on Machine learning (ICML '04). ACM, New York, NY, USA, 23-. DOI: https://doi.org/10.1145/1015330.1015415