{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T07:44:49Z","timestamp":1782373489025,"version":"3.54.5"},"reference-count":24,"publisher":"MIT Press - Journals","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Computation"],"published-print":{"date-parts":[[2014,11]]},"abstract":"<jats:p> Financial risk measures have been used recently in machine learning. For example, [Formula: see text]-support vector machine ([Formula: see text]-SVM) minimizes the conditional value at risk (CVaR) of margin distribution. The measure is popular in finance because of the subadditivity property, but it is very sensitive to a few outliers in the tail of the distribution. We propose a new classification method, extended robust SVM (ER-SVM), which minimizes an intermediate risk measure between the CVaR and value at risk (VaR) by expecting that the resulting model becomes less sensitive than [Formula: see text]-SVM to outliers. We can regard ER-SVM as an extension of robust SVM, which uses a truncated hinge loss. Numerical experiments imply the ER-SVM\u2019s possibility of achieving a better prediction performance with proper parameter setting. <\/jats:p>","DOI":"10.1162\/neco_a_00647","type":"journal-article","created":{"date-parts":[[2014,7,24]],"date-time":"2014-07-24T15:06:10Z","timestamp":1406214370000},"page":"2541-2569","source":"Crossref","is-referenced-by-count":10,"title":["Extended Robust Support Vector Machine Based on Financial Risk Minimization"],"prefix":"10.1162","volume":"26","author":[{"given":"Akiko","family":"Takeda","sequence":"first","affiliation":[{"name":"Department of Mathematical Informatics, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuhei","family":"Fujiwara","sequence":"additional","affiliation":[{"name":"Department of Mathematical Informatics, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takafumi","family":"Kanamori","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Mathematical Informatics, Nagoya University, Chikusa-ku, Nagoya-shi, Aichi 464-8603, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"281","reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9965.00068"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1515\/9781400831050"},{"key":"B3","first-page":"57","volume-title":"Proceedings of the 17th International Conference on Machine Learning","author":"Bennett K. 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