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 Nu-Support Vector Machine as Conditional Value-at-Risk Minimization as author at 25th International Conference on Machine Learning (ICML), Helsinki 2008, 7533 views |