Akiko Takeda
homepage:http://www.ae.keio.ac.jp/lab/soc/takeda/takeda/index.html
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Description

I have studied algorithms that efficiently find solutions to large-scale optimization problems. Optimization refers to decision-making issues that seek methods that bring maximum profits from limited resources. I have endeavored to solve large-scale optimization problems, using computers with efficient algorithms that take advantage of the mathematical features of the problems. My main research interests are:

  • Robust Optimization, Uncertainty
  • Support Vector Machine
  • Parallel Computing
  • Global Optimization
  • Polyhedral Homotopy Continuation Method


Lecture:

lecture
flag Nu-Support Vector Machine as Conditional Value-at-Risk Minimization
as author at  25th International Conference on Machine Learning (ICML), Helsinki 2008,
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