Does avyone know the reason why the Non-Linear SVM has not been implemented in Apache Spark? I was reading this page: https://issues.apache.org/jira/browse/SPARK-4638
Look at the last comment. It says:
"Commenting here b/c of the recent dev list thread: Non-linear kernels for SVMs in Spark would be great to have. The main barriers are: Kernelized SVM training is hard to distribute. Naive methods require a lot of communication. To get this feature into Spark, we'd need to do proper background research and write up a good design. Other ML algorithms are arguably more in demand and still need improvements (as of the date of this comment). Tree ensembles are first-and-foremost in my mind."
The question is: Why is the kernelized SVM hard to distribute?
Everybody knows that the non-linear SVMs exhibit better performance than the linear ones.