图 书 馆
高维监督学习的实证评价An Empirical Evaluation of Supervised Learning in High Dimensions |
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| 课程网址: | http://videolectures.net/icml08_karampatziakis_aee/ |
| 主讲教师: | Nikos Karampatziakis |
| 开课单位: | 康奈尔大学 |
| 开课时间: | 2008-08-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this paper we perform an empirical evaluation of supervised learning methods on high dimensional data. We evaluate learning performance on three metrics: accuracy, AUC, and squared loss. We also study the effect of increasing dimensionality on the relative performance of the learning algorithms. Our findings are consistent with previous studies for problems of relatively low dimension, but suggest that as dimensionality increases the relative performance of the various learning algorithms changes. To our surprise, the methods that seem best able to learn from high dimensional data are random forests and neural nets. |
| 关 键 词: | 高维数据; 实证评估; 平方损失 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-18：cwx |
| 阅读次数: | 86 |