| Optimal reduction of solutions for support vector machines | |
|---|---|
| 學年 | 98 |
| 學期 | 1 |
| 出版（發表）日期 | 2009-08-01 |
| 作品名稱 | Optimal reduction of solutions for support vector machines |
| 作品名稱（其他語言） | |
| 著者 | Lin, Hwei-Jen; Yeh, Jih-Pin |
| 單位 | 淡江大學資訊工程學系 |
| 出版者 | Philadelphia: Elsevier Inc. |
| 著錄名稱、卷期、頁數 | Applied Mathematics and Computation 214(2), pp.329-335 |
| 摘要 | Being a universal learning machine, a support vector machine (SVM) suffers from expensive computational cost in the test phase due to the large number of support vectors, and greatly impacts its practical use. To address this problem, we proposed an adaptive genetic algorithm to optimally reduce the solutions for an SVM by selecting vectors from the trained support vector solutions, such that the selected vectors best approximate the original discriminant function. Our method can be applied to SVMs using any general kernel. The size of the reduced set can be used adaptively based on the requirement of the tasks. As such the generalization/complexity trade-off can be controlled directly. The lower bound of the number of selected vectors required to recover the original discriminant function can also be determined. |
| 關鍵字 | Support vector machine;Vector correlation;Genetic algorithms;Optimal solution;Discriminant function;Pattern recognition |
| 語言 | en |
| ISSN | 0096-3003 |
| 期刊性質 | 國外 |
| 收錄於 | SCI EI |
| 產學合作 | |
| 通訊作者 | Yeh, Jih-Pin |
| 審稿制度 | 是 |
| 國別 | USA |
| 公開徵稿 | |
| 出版型式 | 紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/53759 ) |
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