I know how it feels. SVM's are an old and tried classification method that was in fashion before the DL craze.

> An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall.

One advantage of SVM's is that they don't use all data points to decide on the separation plane, just the closest points to the gap (the support vectors), making it more invariant.

Another advantage is that they can efficiently perform a non linear classification using the kernel trick, implicitly mapping their inputs into high dimensional feature spaces (here kernel means a distance function between two data points).

https://en.wikipedia.org/wiki/Support_vector_machine

https://en.wikipedia.org/wiki/Positive-definite_kernel

I recommend Andrew Ng's ML course for learning about SVM's.

Support Vector Machine. A Machine Learning technique that's used typically for classification and regression, but also has been adapted to novelty detection, stuctured prediction, ranking etc. I wrote up a beginner's tutorial some time back, if you are interested [1].

[1] https://blog.statsbot.co/support-vector-machines-tutorial-c1...

Before I joined the ML team at my company I too had no clue. Now, it is what we talk about 30% of the time :)

Support Vector Machine

I don't want to be too much of an asshole but don't you have Google like the rest of us. I always think it's so weird when people ask something to the comments in HN of all places when they could literally just have textually written it in Wikipedia or wtv and gotten a better answer