Daehyeon Cho

We don’t have enough information about this author to calculate their statistics. If you think this is an error let us know.
Learn More
Support vector quantile regression (SVQR) is capable of providing a good description of the linear and nonlinear relationships among random variables. In this paper we propose a sparse SVQR to overcome a weak point of SVQR, nonsparsity. The asymmetric e-insensitive loss function is used to efficiently provide the sparsity Experimental results are then(More)
  • 1