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

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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.