Since neural nets are winning at the moment, it's easy to see SVMs as an underdog, being ignored due to deep learning hype and PR. This is kind of true, but it's worth noting that 10-15 years ago we had the exact opposite situation. Neural nets were a once promising technique that had stagnated/hit their limits, while SVMs were the new state of the art.
People were coming up with dozens of unnecessary variations on them, everybody in the world was trying to shoehorn the word "kernel" into their paper titles, using some kind of kernel method was a surefire way to get published.
I wish machine learning research didn't respond so strongly to trends and hype, and I also wish the economics of academic research didn't force people into cliques fighting over scarce resources.
I'm still wondering what, if anything, is going to supplant deep learning. It's probably an existing technique that will suddenly become much more usable due to some small improvement.
People were coming up with dozens of unnecessary variations on them, everybody in the world was trying to shoehorn the word "kernel" into their paper titles, using some kind of kernel method was a surefire way to get published.
I wish machine learning research didn't respond so strongly to trends and hype, and I also wish the economics of academic research didn't force people into cliques fighting over scarce resources.
I'm still wondering what, if anything, is going to supplant deep learning. It's probably an existing technique that will suddenly become much more usable due to some small improvement.