图 书 馆
最优规则组合的l1正则化框架An l1 Regularization Framework for Optimal Rule Combination |
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| 课程网址: | http://videolectures.net/ecmlpkdd09_han_l1rforc/ |
| 主讲教师: | Yanjun Han |
| 开课单位: | 中国科学院 |
| 开课时间: | 2009-10-20 |
| 课程语种: | 汉简 |
| 中文简介: |  |
| 课程简介: | In this paper l1 regularization is introduced into relational learning to produce sparse rule combination. In other words, as few as possible rules are contained in the final rule set. Furthermore, we design a rule complexity penalty to encourage rules with fewer literals. The resulted optimization problem has to be formulated in an infinite dimensional space of horn clauses $R_m$ associated with their corresponding complexity $\mathcal{C}_m$. It is proved that if a locally optimal rule is generated at each iteration, the final obtained rule set will be globally optimal. The proposed meta-algorithm is applicable to any single rule generator. We bring forward two algorithms, namely, $\ell_1$FOIL and $\ell_1$Progol. Empirical analysis is carried on ten real world tasks from bioinformatics and cheminformatics. The results demonstrate that our approach offers competitive prediction accuracy while the interpretability is straightforward. |
| 关 键 词: | l1正则化; 关系学习; 最优规则 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-03-27：lxf |
| 阅读次数: | 68 |