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parameters.md

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ThunderSVM Parameters

This page is for parameter specification in ThunderSVM. The parameters used in ThunderSVM are identical to LibSVM, so existing LibSVM users can easily get used to ThunderSVM.

command line options:

  • -s: set the type of SVMs (default=0)

    • 0 -- C-SVC
    • 1 -- $ \nu $-SVC
    • 2 -- one-class SVMs
    • 3 -- $ \epsilon $-SVR
    • 4 -- $ \nu $-SVR

  • -t: set the type of kernel function (default=2)

    • 0 -- linear: $ \boldsymbol{x}_i^T \cdot \boldsymbol{x}_j $
    • 1 -- polynomial: $ (\gamma \boldsymbol{x}_i^T \cdot \boldsymbol{x}_j + r)^d $
    • 2 -- radial basis function (RBF): $ \exp(-\gamma ||\boldsymbol{x}_i - \boldsymbol{x}_j||^2) $
    • 3 -- sigmoid: $ tanh(\gamma \boldsymbol{x}_i^T \cdot \boldsymbol{x}_j+ r) $

  • -d: set the degree in kernel function (default=3)

  • -g: set $ gamma $ in kernel function (default=$ \frac{1}{\text{num_features}} $)

  • -r: set $ r $ in kernel function (default=0)

  • -c: set the parameter C of C-SVC, $ \epsilon $-SVR, and $ \nu $-SVR (default=1)

  • -n: set the parameter $ \nu $ of $ \nu $-SVC, one-class SVM, and $ \nu $-SVR (default=0.5)

  • -p: set the $ \epsilon $ in loss function of $ \epsilon $-SVR (default=0.1)

  • -m: set cache memory size in MB (default=100)

  • -e: set tolerance of termination criterion (default=0.001)

  • -h: whether to use the shrinking heuristics, 0 or 1 (default=1)

  • -b: whether to train probabilistic SVC or SVR, 0 or 1 (default=0)

  • -wi: for weighted C-SVC, set the parameter C of class i to $ wi \times C $ (default=1)

  • -v n: n-fold cross validation mode

  • -u n: specify which gpu to use (default=0)

The options in italic are not applicable for GPUs, and the alternative optimizations are implemented with automatically setting working set size.