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详细书目
| 文件类型: | 文章 |
|---|---|
| 所有的著者/提供者: | T B Trafalis; R C Gilbert |
| ISSN: | 0377-2217 |
| OCLC号码: | 442781502 |
| 语言注释: | English |
| 奖励: |
摘要:
In this paper, we investigate the theoretical aspects of robust classification and robust regression using support vector machines. Given training data (x"1,y"1),...,(x"l,y"l), where l represents the number of samples, x"i@?R^n and y"i@?{-1,1} (for classification) or y"i@?R (for regression), we investigate the training of a support vector machine in the case where bounded perturbation is added to the value of the input x"i@?R^n. We consider both cases where our training data are either linearly separable and nonlinearly separable respectively. We show that we
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