图 书 馆
半监督提升的信息论正则化Information Theoretic Regularization for Semi-Supervised Boosting |
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| 课程网址: | http://videolectures.net/kdd09_zheng_itrssb/ |
| 主讲教师: | Lei Zheng |
| 开课单位: | 德克萨斯大学 |
| 开课时间: | 2001-09-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We present novel semi-supervised boosting algorithms that incrementally build linear combinations of weak classifiers through generic functional gradient descent using both labeled and unlabeled training data. Our approach is based on extending information regularization framework to boosting, bearing loss functions that combine log loss on labeled data with the information-theoretic measures to encode unlabeled data. Even though the information-theoretic regularization terms make the optimization non-convex, we propose simple sequential gradient descent optimization algorithms, and obtain impressively improved results on synthetic, benchmark and real world tasks over supervised boosting algorithms which use the labeled data alone and a state-of-the-art semi-supervised boosting algorithm. |
| 关 键 词: | 半监督学习; 算法; 信息理论 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2023-12-29：liyy |
| 阅读次数: | 68 |