Nonconvex classification method based on risk minimization and its application to credit approvals and medical diagnosis
| Project/Area Number |
19710124
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Social systems engineering/Safety system
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| Research Institution | Keio University (2008-2010)
Tokyo Institute of Technology (2007) |
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Principal Investigator |
TAKEDA Akiko Keio University, 理工学部, 講師 (80361799)
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| Project Period (FY) |
2007 – 2010
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| Project Status |
Completed (Fiscal Year 2010)
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| Budget Amount *help |
¥3,920,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥720,000)
Fiscal Year 2010: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2009: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2008: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2007: ¥800,000 (Direct Cost: ¥800,000)
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| Keywords | OR / 数理最適化 / 金融工学 / サポートベクターマシーン / 分類問題 / リスク尺度 / conditional value-at-risk / ロバスト最適化 / ポートフォリオ最適化 / 統計的学習 / 汎化性能 / Conditional Value-at-Risk / 汎化誤差 / 機械学習 / 非凸二次計画問題 / 局所最適性 |
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| Research Abstract |
Using the existing studies on mathematical optimization, financial engineering and machine learning, I theoretically evaluated the prediction performance of a classification method known as Eν-SVM. The SVM has been quite successful in practice. However, no satisfactory theoretical background existed so far. We provided such background and also explain how this nonconvex optimization problem can actually be solved. Moreover, we adopted the concept of "regularization term" that is often used in machine learning for portfolio optimization problems in financial engineering and succeeded in enhancing the prediction performance of portfolio optimization models.
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Report
(6 results)
Research Products
(53 results)
