SVM algorithm is quite old – it was developed 1960s, but was extremely popular in 1990s and 2000s. It is a classical (and quite beautiful) part of machine learning courses.
Today it seems that in media processing (images, sound etc.) neural networks completely dominate, while in other areas Gradient Boosting has very strong positions.
Also, in recent data competitions I observe no SVM-based solutions.
I am looking for application examples where SVM still gives state-of-art results (as of 2016).
Update: I'd like to have some example which I can give e.g. to students / colleagues when explaining SVM so that it doesn't look like purely theoretical or deprecated approach.
Best Answer
According to the paper Do we Need Hundreds of Classifiers to Solve Real World Classification Problems? SVM together with Random Forest and Gradient Booting Machines are among the top performing classification algorithms for a large set of 120+ datasets (using accuracy as metric).
I repeated their experiments with some modifications and I get these three classifiers performing better than the others, but as the no free lunch theorem says there are always a problem where some other algorithm performs better than these three.
So yes, I would say that SVM (with Gaussian kernel - that is what I used) is still a relevant algorithm for non-media related datasets.