图 书 馆
基于概率预测的支持向量回归特征选择Feature Selection for Support Vector Regression Using Probabilistic Prediction |
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| 课程网址: | http://videolectures.net/kdd2010_yang_fssv/ |
| 主讲教师: | Jian-Bo Yang |
| 开课单位: | 新加坡国立大学 |
| 开课时间: | 2010-10-01 |
| 课程语种: | 英语 |
| 中文简介: |  本文提出了一种基于新包装的基于特征选择的支持向量回归（SVR）的概率预测方法。该方法通过在具有和不具有特征的情况下，在特征空间上聚合SVR预测的条件密度函数之间的差异，来计算特征的重要性。由于此重要程度的精确计算成本很高，因此提出了两种近似方法。与其他几种用于SVR的现有特征选择方法相比，使用这些近似方法对措施的有效性在人工和现实问题上均进行了评估。实验结果表明，该方法在数据稀疏的情况下总体上表现较好，至少与现有方法相比具有明显的优势。 |
| 课程简介: | This paper presents a novel wrapper-based feature selection method for Support Vector Regression (SVR) using its probabilistic predictions. The method computes the importance of a feature by aggregating the difference, over the feature space, of the conditional density functions of the SVR prediction with and without the feature. As the exact computation of this importance measure is expensive, two approximations are proposed. The effectiveness of the measure using these approximations, in comparison to several other existing feature selection methods for SVR, is evaluated on both artificial and real-world problems. The result of the experiment shows that the proposed method generally performs better, and at least as well as the existing methods, with notable advantage when the data set is sparse. |
| 关 键 词: | 向量回归; 精确计算 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-05-08：zyk |
| 最后编审: | 2021-05-08：zyk |
| 阅读次数: | 88 |