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Huber loss function can suppress a variety of noise and outliers and yields great generalization performance. Motivated by this, we propose a novel twin support vector regression with Huber loss for the noise data in this paper. Experiments on nine benchmark datasets with different Gaussian noise show the validity of our proposed algorithm. Finally, we apply our method to the financial time series data that usually contain noise and outlier, and it also produces great performance.\n                  <\/jats:p>","DOI":"10.3233\/jifs-16629","type":"journal-article","created":{"date-parts":[[2017,5,26]],"date-time":"2017-05-26T14:43:11Z","timestamp":1495809791000},"page":"4247-4258","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":16,"title":["Twin support vector regression with\u00a0Huber\u00a0loss"],"prefix":"10.1177","volume":"32","author":[{"given":"Jiayi","family":"Niu","sequence":"first","affiliation":[{"name":"College of Science, China Agricultural University, Beijing, China"}]},{"given":"Jing","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Science, China Agricultural University, Beijing, China"}]},{"given":"Yitian","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Science, China Agricultural 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