It's always hard to get a birds'-eye view of these things, but from my vantage point, I think SVMs are much more widely used in applications, but have been surp

It's always hard to get a birds'-eye view of these things, but from my vantage point, I think SVMs are much more widely used in applications, but have been surpassed by deep learning as the current hot research area in machine learning. There is still SVM research, but it's plateaued compared to the frenzy of the late 1990s and early 2000s, while deep learning has taken off in the past few years. SVM has instead entered the realm of "standard ML tool", as an off-the-shelf technique with robust and well-documented implementations that can be applied in a reasonably straightforward way.