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功能的最佳数量是多少？从学习理论视角解读What is the Optimal Number of Features? A learning theoretic perspective |
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| 课程网址: | http://videolectures.net/slsfs05_navot_wonfl/ |
| 主讲教师: | Amir Navot |
| 开课单位: | 耶路撒冷希伯来大学 |
| 开课时间: | 2007-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this paper we discuss the problem of feature selection for supervised learning from the standpoint of statistical machine learning. We inquire what subset of features will lead to the best classification accuracy. It is clear that if the statistical model is known, or if there are an unlimited number of training samples, any additional feature can only improve the accuracy. However, we explicitly show that when the training set is finite, using all the features may be suboptimal, even if all the features are independent and carry information on the label. We analyze one setting analytically and show how feature selection can increase accuracy. We also find the optimal number of features as a function of the training set size for a few specific examples. This perspective on feature selection is different from the common approach that focuses on the probability that a specific algorithm will pick a completely irrelevant or redundant feature. |
| 关 键 词: | 机器学习; 特征子集; 训练集 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-21：cwx |
| 阅读次数: | 78 |