In our last tutorial on SVM training with GPU, we mentioned a necessary step to
pre-scale the data with rpusvm-scale, and to reverse scaling the prediction outcome.
This cumbersome procedure is now simplified with the latest RPUSVM.
Most elementary statistical inference algorithms assume that the data can be modeled by linear parameters with a normally distributed error component. A new class of algorithms called support vector machine (SVM) remove such constraint.
