[sterlind]

do people actually use SVMs anymore?

like, regression, sure - because it's a tool to measure how well a hypothesis (polynomial function) matches the data (points.) and CNNs are still foundational in computer vision. but the first and last time I heard of SVMs was in college, by professors who were weirdly dismissive of these newfangled deep neural networks, and enamored by the "kernel trick."

but aren't SVMs basically souped up regression models? are they used in anything ML-esque, i.e. besides validating a hypothesis about the behavior of a system?

[superdisk]

> but the first and last time I heard of SVMs was in college, by professors who were weirdly dismissive of these newfangled deep neural networks, and enamored by the "kernel trick."

LOL. Exact same experience in my college courses. Glad to know it's universal.

[teruakohatu]

> do people actually use SVMs anymore?

Yes they are. They allow for non-linear decision boundaries and more dimensions than rows of data, which for many other ML methods is a problem.

Linear regression, logistic regression, SVM and CART decision trees are all still very popular in the real world where data is hard to come by.

[jacksnipe]

We loved them in medical testing. Very explainable models.