图 书 馆
多阶段多任务特征学习Multi-Stage Multi-Task Feature Learning |
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| 课程网址: | http://videolectures.net/machine_zhang_learning/ |
| 主讲教师: | Changshui Zhang |
| 开课单位: | 清华大学 |
| 开课时间: | 2013-01-15 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Multi-task sparse feature learning aims to improve the generalization performance by exploiting the shared features among tasks. It has been successfully applied to many applications including computer vision and biomedical informatics. Most of the existing multi-task sparse feature learning algorithms are formulated as a convex sparse regularization problem, which is usually suboptimal, due to its looseness for approximating an ℓ0-type regularizer. In this paper, we propose a non-convex formulation for multi-task sparse feature learning based on a novel regularizer. To solve the non-convex optimization problem, we propose a Multi-Stage Multi-Task Feature Learning (MSMTFL) algorithm. Moreover, we present a detailed theoretical analysis showing that MSMTFL achieves a better parameter estimation error bound than the convex formulation. Empirical studies on both synthetic and real-world data sets demonstrate the effectiveness of MSMTFL in comparison with the state of the art multi-task sparse feature learning algorithms. |
| 关 键 词: | 多任务稀疏; 共享特征; 泛化性能 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2021-01-29：nkq |
| 阅读次数: | 103 |