图 书 馆
特征的线性支持向量机的成型Feature Shaping for Linear SVM Classifiers |
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| 课程网址: | http://videolectures.net/kdd09_scholz_fslsvmc/ |
| 主讲教师: | Martin Scholz |
| 开课单位: | 惠普公司 |
| 开课时间: | 2009-09-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Linear classifiers have been shown to be effective for many discrimination tasks. Irrespective of the learning algorithm itself, the final classifier has a weight to multiply by each feature. This suggests that ideally each input feature should be linearly correlated with the target variable (or anti-correlated), whereas raw features may be highly non-linear. In this paper, we attempt to re-shape each input feature so that it is appropriate to use with a linear weight and to scale the different features in proportion to their predictive value. We demonstrate that this pre-processing is beneficial for linear SVM classifiers on a large benchmark of text classification tasks as well as UCI datasets. |
| 关 键 词: | 线性分类器; 高度非线性; 线性重量和规模 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-09-17：chenxin |
| 阅读次数: | 247 |