Earlier quoted context omitted.
This is true, but only in the academic research world. SVMs had relatively little success on practical problems and in industry, so they never built up the kind of standing that neural networks did. Even in 2003-2005 - arguably the peak time for SVMs - neural networks were much better known to almost everyone (industry practitioners, researchers, and laypeople) than SVMs. What frustrates me is that people who are sta…
What prevented SVMs from catching on in industry?
I've also conducted multiple training sessions/discussions with small groups on ML, and it supports what I've said. SVMs are hard to explain to a general crowd; with ANNs I've enough visual cues to get them started. Sure, they mightn't get the math right away, but they understand enough to be comfortable using a library.
As an aside, a comment here mentions Logistic/linear regression dominates the industry. I think it is for a similar reason. They're simple to understand and try out. That doesn't make them good models, in my experience, on a bunch of real-world problems.
Now if you ask me about the technical cons of SVMs, I'd say - scalability of non-linear kernels and the fact that I've to cherry-pick kernels. Linear and RBF kernels work well on most problems, but even then, for a bunch of problems where RBF seems to work well, the number of support vectors stored by the model can be massive. If I weren't to be pedantic about it and excuse the fact that the kernel seems to be "memorizing" more than "learning", this is still a beast to run in real time. nu-SVMs address this issue to an extent, but then we are back to picking the right kernel for the task. This is one thing I love about ANNs - the kernel (or what essentially is the kernel) is learned.