I’m currently learning about classifiers.
Our dear professor linked us this paper from 2014: http://jmlr.org/papers/volume15/delgado14a/delgado14a.pdf
Which claims that Support Vector Machines is the most efficient way of classifying these datasets - even compared to neural networks.
I’m thinking that this paper from 2014 might be outdated, but haven’t yet found a recent comparison.
I realize the point that if the size of the training data is small then generative learning methods might be more efficient.
So, I’m curious. Does anyone have any recent result comparing any of these datasets using modern technologies compared to SVM and such?
(Unfortunately the exact link to the datasets mentioned in the paper [http://archive.ics.uci.edu/ml/datasets.html?task=cla] is not available anymore.)
Thanks in advance!