Nonconvex classification method based on risk minimization and its application to credit approvals and medical diagnosis

基于风险最小化的非凸分类方法及其在信贷审批和医疗诊断中的应用

• 批准号: 19710124
• 负责人: TAKEDA Akiko
• 金额: $ 2.51万
• 依托单位: Keio University (2008-2010)Tokyo Institute of Technology (2007)
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Young Scientists (B)
• 财政年份: 2007
• 资助国家: 日本
• 起止时间: 2007 至 2010
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-19710124/
• 关键词: 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

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.

利用数学优化、金融工程和机器学习方面的现有研究，我从理论上评估了称为 Eν-SVM 的分类方法的预测性能，但迄今为止我们还没有提供令人满意的理论背景。此外，我们采用了金融工程中投资组合优化问题的机器学习中经常使用的“正则化项”概念，并成功地提高了投资组合优化的预测性能。模型。

项目成果

A robust approach based on conditional value-at-risk measure to statistical learning problems

• DOI: 10.1016/j.ejor.2008.07.027
• 发表时间: 2009-10
• 期刊: Eur. J. Oper. Res.
• 作者: Akiko Takeda;Takafumi Kanamori

太陽光発電システム導入計画に対するロバスト最適化モデル

太阳能发电系统引入规划鲁棒优化模型

• 发表时间: 2010
• 作者: 大城戸慎平(発表者);武田朗子
• 通讯作者: 武田朗子

Adjustable Robust Optimization Models for a Nonlinear Two-Period System

• DOI: 10.1007/s10957-007-9288-8
• 发表时间: 2008-02
• 期刊: Journal of Optimization Theory and Applications
• 影响因子: 1.9
• 作者: A. Takeda;S. Taguchi;R. Tütüncü

Support vector regression as conditional value-at-risk minimization with application to financial time-series analysis

• DOI: 10.1109/mlsp.2010.5589245
• 发表时间: 2010-10
• 期刊: 2010 IEEE International Workshop on Machine Learning for Signal Processing
• 作者: A. Takeda;Jun-ya Gotoh;Masashi Sugiama

On generalization performance and non-convex optimization of extended \nu-support vector machine

扩展 u支持向量机的泛化性能与非凸优化

• 发表时间: 2009
• 期刊: New Generation Computing 27
• 作者: Akiko Takeda;Masashi Sugiyama
• 通讯作者: Masashi Sugiyama