Name
Abstract | ||
|---|---|---|
The purpose of this article is to review the similarity and difference between financial risk minimization and a class of machine learning methods known as support vector machines, which were independently developed. By recognizing their common features, we can understand them in a unified mathematical framework. On the other hand, by recognizing their difference, we can develop new methods. In particular, employing the coherent measures of risk, we develop a generalized criterion for two-class classification. It includes existing criteria, such as the margin maximization and \(\nu \)-SVM, as special cases. This extension can also be applied to the other type of machine learning methods such as multi-class classification, regression and outlier detection. Although the new criterion is first formulated as a nonconvex optimization, it results in a convex optimization by employing the nonnegative \(\ell _1\)-regularization. Numerical examples demonstrate how the developed methods work for bond rating. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s10287-013-0175-5 | Comput. Manag. Science |
Keywords | Field | DocType |
credit rating | Financial risk,Anomaly detection,Mathematical optimization,Regression,Support vector machine,Credit rating,Minification,Artificial intelligence,Bond credit rating,Convex optimization,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
11 | 4 | 1619-6988 |
Citations | PageRank | References |
4 | 0.44 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun-Ya Gotoh | 1 | 117 | 10.17 |
| Akiko Takeda | 2 | 196 | 29.72 |
| Rei Yamamoto | 3 | 37 | 4.31 |