SVM performance using laplace kernel is too slow

classic Classic list List threaded Threaded
1 message Options
Reply | Threaded
Open this post in threaded view
|

SVM performance using laplace kernel is too slow

optiontrader1138
I've created an SVM in R using the kernlab package, however it's running incredibly slow (20,000 predictions takes ~45 seconds on win64 R distribution). CPU is running at 25% and RAM utilization is a mere 17% ... it's not a hardware bottleneck. Similar calculations using data mining algorithms in SQL Server analysis services run about 40x faster.



Through trial and error, we discovered that the laplacedot kernel gives us the best results by a wide margin. Rbfdot is about 15% less accurate, but twice as fast (but still too slow). The best performance is vanilladot. It runs more or less instantly but the accuracy is way too low to use.



We'd ideally like to use the laplacedot kernel but to do so we need a massive speedup. Does anyone have any ideas on how to do this?



Here is some profiling information I generated using rprof. It looks like most of the time is spent in low level math calls (the rest of the profile consists of similar data as rows 16-40). This should run very quickly but it looks like the code is just not optimized (and I don't know where to start). Most notably, some of the subtraction methods take around .30 seconds... something must be wrong!



http://pastebin.com/yVPC66Be

        [[alternative HTML version deleted]]

______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.