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Data Science Seminar
Hosted by Department of Mathematical Sciences

  • Date: Tuesday, April 17, 2018
  • Time: 12:05pm – 1:05pm
  • Room: WH-100E
  • Speaker: Wenbo Wang (Binghamton University)
  • Title: A look at distance-weighted discrimination


Distance-weighted discrimination (DWD) is a modern margin-based classifier with an interesting geometric motivation. It was proposed as a competitor to the support vector machine (SVM). The authors greatly advance the current DWD methodology and its learning theory. They propose a novel thrifty algorithm for solving standard DWD and generalized DWD. Furthermore, they formulate a natural kernel DWD approach in a reproducing kernel Hilbert space and then establish the Bayes risk consistency of the kernel DWD by using a universal kernel such as the Gaussian kernel.

seminars/datasci/180417.txt · Last modified: 2018/04/16 15:38 by gang