图 书 馆
结构稀疏的增压Boosting with Structural Sparsity |
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| 课程网址: | http://videolectures.net/icml09_duchi_bwss/ |
| 主讲教师: | John Duchi |
| 开课单位: | 加州大学 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We derive generalizations of AdaBoost and related gradient-based coordinate descent methods that incorporate sparsity-promoting penalties for the norm of the predictor that is being learned. The end result is a family of coordinate descent algorithms that integrate forward feature induction and back-pruning through regularization and give an automatic stopping criterion for feature induction. We study penalties based on the ℓ1 , ℓ2 , and ℓ ∞ norms of the predictor and introduce mixed-norm penalties that build upon the initial penalties. The mixed-norm regularizers facilitate structural sparsity in parameter space, which is a useful property in multiclass prediction and other related tasks. We report empirical results that demonstrate the power of our approach in building accurate and structurally sparse models. |
| 关 键 词: | 结构稀疏; 坐标下降; 参数空间 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-13：chenjy |
| 最后编审: | 2023-03-13：chenjy |
| 阅读次数: | 29 |