Re:A Quick Look at Support Vector Machines
by samuell on Thursday March 23, 2017 @12:10PM
(#13941062)
We found great success with the LIBLINEAR SVM implementation [1] though: Extremely good performance, to the point that it affects scalability too, with predictive performance acceptably close to libSVM with the RBF kernel, for a large cheminformatics dataset:
As can be seen in fig 5 [2] in the paper, a dataset size that took ~1 week with libSVM (actually, the parallel piSVM implementation) on 64 cores, took less than a minute with LIBLINEAR, which runs on just one core.
Re:A Quick Look at Support Vector Machines
by Fede_V on Thursday March 23, 2017 @04:35PM
(#13944001)
With an RBF Kernel, you are stuck solving the dual problem which has O(n^2 * m) complexity. With the linear kernel, you can solve the primal scales with O(n*m) complexity.
Paper (open acccess): http://dx.doi.org/10.1186/s13321-016-0151-5
As can be seen in fig 5 [2] in the paper, a dataset size that took ~1 week with libSVM (actually, the parallel piSVM implementation) on 64 cores, took less than a minute with LIBLINEAR, which runs on just one core.
[1] https://www.csie.ntu.edu.tw/~cjlin/liblinear
[2] http://jcheminf.springeropen.com/articles/10.1186/s13321-016...