图 书 馆
随机分类噪声击败所有凸势的助推器Random Classification Noise Defeats All Convex Potential Boosters |
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| 课程网址: | http://videolectures.net/icml08_long_rcn/ |
| 主讲教师: | Phil Long |
| 开课单位: | 美国感知技术有限公司 |
| 开课时间: | 2008-08-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | A broad class of boosting algorithms can be interpreted as performing coordinate-wise gradient descent to minimize some potential function of the margins of a data set. This class includes AdaBoost, LogitBoost, and other widely used and well-studied boosters. In this paper we show that for a broad class of convex potential functions, any such boosting algorithm is highly susceptible to random classification noise. We do this by showing that for any such booster and any nonzero random classification noise rate R, there is a simple data set of examples which is efficiently learnable by such a booster if there is no noise, but which cannot be learned to accuracy better than 1/2 if there is random classification noise at rate R. This negative result is in contrast with known branching program based boosters which do not fall into the convex potential function framework and which can provably learn to high accuracy in the presence of random classification noise. |
| 关 键 词: | 数据集; 随机分类噪声; 助推器; 凸势函数 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-23：liqy |
| 阅读次数: | 38 |