图 书 馆
关于大量量词的机会准确性On the Chance Accuracies of Large Collections of Classifiers |
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| 课程网址: | http://videolectures.net/icml08_palatucci_oca/ |
| 主讲教师: | Mark Palatucci |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2008-08-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We provide a theoretical analysis of the chance accuracies of large collections of classifiers. We show that on problems with small numbers of examples, some classifier can perform well by random chance, and we derive a theorem to explicitly calculate this accuracy. We use this theorem to provide a principled feature selection criteria for sparse, high-dimensional problems. We evaluate this method on both microarray and fMRI datasets and show that it performs very close to the optimal accuracy obtained from an oracle. We also show that on the fMRI dataset this technique chooses relevant features successfully while another state-of-the-art method, the False Discovery Rate (FDR), completely fails at standard significance levels. |
| 关 键 词: | 分类器; 高维问题; 微阵列 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-22：chenxin |
| 阅读次数: | 57 |