| Título: | An Improved Twin Support Vector Regression with Automatic Margin Determination |
| Autores: |
Jun, LIANG; Jiangsu University Zhi-qiang, SHA; Jiangsu University Ying-wen, REN; Jiangsu University Ao-xue, LI; Jiangsu University Long, CHEN; Jiangsu University |
| Fecha: | 2013-01-01 |
| Publicador: | TELKOMNIKA: Indonesian journal of electrical engineering |
| Fuente: |
 |
| Tipo: |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| Tema: | No aplica |
| Descripción: | In this paper, a novel regression algorithm named ν-twin support vector regression (ν-TSVR) is presented, improving upon the recently proposed twin support vector regression (TSVR). It also tries to seek two nonparallel down- and up-bounds for the unknown function. By treating the size of one-sided -insensitive tube as optimization variables with corresponding parameters s, we reformulate the original TSVR as a more sensible model. To this end, ν-TSVR has the advantage that s are learned simultaneously with regressor. Meantime, we give a theoretical result concerning the meaning of s. Moreover, by introducing structural risk minimization principle, the over-fitting phenomenon in TSVR can be avoided. We analyze the algorithm theoretically and demonstrate its effectiveness via the experimental results on several artificial and benchmark datasets. |
| Idioma: | Inglés |