图 书 馆
机器学习中的特征选择降维Dimensionality Reduction by Feature Selection in Machine Learning |
|
| 课程网址: | http://videolectures.net/slsfs05_mladenic_drfsm/ |
| 主讲教师: | Dunja Mladenić |
| 开课单位: | 约瑟夫·斯特凡学院 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Dimensionality reduction is a commonly used step in machine learning, especially when dealing with a high dimensional space of features. The original feature space is mapped onto a new, reduced dimensioanllyity space and the examples to be used by machine learning algorithms are represented in that new space. The mapping is usually performed either by selecting a subset of the original features or/and by constructing some new features. This persentation deals with the first approach, feature subset selection. We provide a brief overview of the feature subset selection techniques that are commonly used in machine learning and give a more detailed description of feature subset selection used in machine learning on text data. Performance of some methods used is document categorization is illustrated by providing experimental comparison on real-world data collected from the Web. |
| 关 键 词: | 计算机科学; 机器学习; 预处理 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-07-28：yumf |
| 阅读次数: | 25 |