Algebraic multigrid support vector machines

نویسندگان

  • Ehsan Sadrfaridpour
  • Sandeep Jeereddy
  • Ken Kennedy
  • André Luckow
  • Talayeh Razzaghi
  • Ilya Safro
چکیده

The support vector machine is a flexible optimization-based technique widely used for classification problems. In practice, its training part becomes computationally expensive on large-scale data sets because of such reasons as the complexity and number of iterations in parameter fitting methods, underlying optimization solvers, and nonlinearity of kernels. We introduce a fast multilevel framework for solving support vector machine models that is inspired by the algebraic multigrid. Significant improvement in the running has been achieved without any loss in the quality. The proposed technique is highly beneficial on imbalanced sets. We demonstrate computational results on publicly available and industrial data sets.

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عنوان ژورنال:
  • CoRR

دوره abs/1611.05487  شماره 

صفحات  -

تاریخ انتشار 2016