[verroq]

SVMs are as old school ML as they get. They guarentee the maximum separation at the decision boundary. However it doesn’t scale very well for higher dimensional data. The standard used to be to use some dimensionality reduction technique like PCA to preprocess before feeding it into the SVM.

This is all before deep learning.

[ufo]

Exactly. Perhaps the paper could have given a clearer message if the abstract had characterized SVMs as a quadratic optimization technique instead of as machine learning?