图 书 馆
无附加标记数据的决策边界学习动力学Learning Dynamics of Decision Boundaries without Additional Labeled Data |
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| 课程网址: | http://videolectures.net/kdd2018_kumagai_dynamics_decision/ |
| 主讲教师: | Atsutoshi Kumagai |
| 开课单位: | 日本电报电话公司（NTT） |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We propose a method for learning the dynamics of the decision boundary to maintain classification performance without additional labeled data. In various applications, such as spam-mail classification, the decision boundary dynamically changes over time. Accordingly, the performance of classifiers deteriorates quickly unless the classifiers are retrained using additional labeled data. However, continuously preparing such data is quite expensive or impossible. The proposed method alleviates this deterioration in performance by using newly obtained unlabeled data, which are easy to prepare, as well as labeled data collected beforehand. With the proposed method, the dynamics of the decision boundary is modeled by Gaussian processes. To exploit information on the decision boundaries from unlabeled data, the low-density separation criterion, i.e., the decision boundary should not cross high-density regions, but instead lie in low-density regions, is assumed with the proposed method. We incorporate this criterion into our framework in a principled manner by introducing the entropy posterior regularization to the posterior of the classifier parameters on the basis of the generic regularized Bayesian framework. We developed an efficient inference algorithm for the model based on variational Bayesian inference. The effectiveness of the proposed method was demonstrated through experiments using two synthetic and four real-world data sets. |
| 关 键 词: | 学习决策边界动态的方法; 垃圾邮件分类; 穿过高密度区域; 变分贝叶斯推理 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-01-30：cyh |
| 最后编审: | 2023-01-30：cyh |
| 阅读次数: | 8 |