Hi
I am using the scikitlearn implementation of Nu-SVR.
My problem (automatic phonetic segmentation for singing voice) has ~ 50k
points with 36 features. Seems relatively small to me compared to the
datasets I have been reading about. The problem is it takes a long time (~6
hours) to fit the NuSVR model to 60% of this data and coarse gridsearching
for the parameters obviously takes even longer (a couple of days). The
times are with gridsearching with the njobs=-1 option enabled on a 4 core
machine.
How can I use joblibs to parallelise NuSVR ? Can it be done at all ?
Any other ideas to speedup what I am doing ?
Maybe there is some basic fault in my code <http://pastebin.com/50AUePkt> ,
since I am very new to this. For anyone interested,
this<http://neural.cs.nthu.edu.tw/jang/research/paper/2007-ieee-taslp/IEEE2007.pdf>is
the inspiration for our approach.
PS: I made a reddit
thread<http://www.reddit.com/r/MachineLearning/comments/1fvk15/ways_to_speedup_svr_training_in_scikitlearn/>as
well.
thanks!
Varun Jewalikar
www.vjewalikar.in
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